Hybrid Arima - LSTM code - Full Dataset

The hybrid ARIMA-LSTM model is open to a variety of experimentation. For ideal performance, a balance must be reached between the levels of volatility that work best for the ARIMA and LSTM models. Using shorter MA periods that result in a non-mesokurtic distribution may achieve a better volatility balance between models.

Import Libraries

In [1]:
import pandas as pd
pd.set_option('display.max_rows', 500)
import timeit
In [2]:
!pip install -q -U keras-tuner
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In [3]:
import keras_tuner as kt
In [4]:
!pip install pmdarima
Collecting pmdarima
  Downloading pmdarima-1.8.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.4 MB)
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Collecting statsmodels!=0.12.0,>=0.11
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Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.1.5)
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Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.2)
Installing collected packages: statsmodels, pmdarima
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    Found existing installation: statsmodels 0.10.2
    Uninstalling statsmodels-0.10.2:
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Successfully installed pmdarima-1.8.4 statsmodels-0.13.1
In [5]:
import pmdarima
In [6]:
url = 'https://launchpad.net/~mario-mariomedina/+archive/ubuntu/talib/+files'
!wget $url/libta-lib0_0.4.0-oneiric1_amd64.deb -qO libta.deb
!wget $url/ta-lib0-dev_0.4.0-oneiric1_amd64.deb -qO ta.deb
!dpkg -i libta.deb ta.deb
!pip install ta-lib
import talib
Selecting previously unselected package libta-lib0.
(Reading database ... 155222 files and directories currently installed.)
Preparing to unpack libta.deb ...
Unpacking libta-lib0 (0.4.0-oneiric1) ...
Selecting previously unselected package ta-lib0-dev.
Preparing to unpack ta.deb ...
Unpacking ta-lib0-dev (0.4.0-oneiric1) ...
Setting up libta-lib0 (0.4.0-oneiric1) ...
Setting up ta-lib0-dev (0.4.0-oneiric1) ...
Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Processing triggers for libc-bin (2.27-3ubuntu1.3) ...
/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link

Collecting ta-lib
  Downloading TA-Lib-0.4.22.tar.gz (268 kB)
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  Installing build dependencies ... done
  Getting requirements to build wheel ... done
    Preparing wheel metadata ... done
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ta-lib) (1.19.5)
Building wheels for collected packages: ta-lib
  Building wheel for ta-lib (PEP 517) ... done
  Created wheel for ta-lib: filename=TA_Lib-0.4.22-cp37-cp37m-linux_x86_64.whl size=1465698 sha256=e0ee59ea3a3a2be9f8d64677fdd5cc7390a01e917590faa1e5d572749cb1727b
  Stored in directory: /root/.cache/pip/wheels/7b/63/a9/144081748d9c4f0a09b4670c7b3c414bcb33ff97f0724c753a
Successfully built ta-lib
Installing collected packages: ta-lib
Successfully installed ta-lib-0.4.22
In [7]:
import tensorflow
import statsmodels.tsa.api
import keras
import sklearn
In [8]:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout, Bidirectional,BatchNormalization, Embedding, TimeDistributed, LeakyReLU, GRU
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
In [9]:
from keras.models import Sequential, load_model
from keras.layers import Dense, LSTM, Activation, Dropout
from keras import backend as K
from keras.utils.generic_utils import get_custom_objects
from keras.callbacks import ModelCheckpoint,EarlyStopping
from keras.regularizers import l1_l2
In [10]:
import math
In [11]:
from statsmodels.tsa.api import VAR
from statsmodels.tsa.statespace.varmax import VARMAX,VARMAXResults
In [12]:
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, mean_absolute_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
In [13]:
from matplotlib import pyplot
In [14]:
import json
import datetime
import pandas as pd
import numpy as np
import os
from scipy.stats import kurtosis
import pmdarima as pm
from pmdarima import auto_arima
from talib import abstract
import json
import matplotlib.pyplot as plt
# plt.rcParams.update({'font.size': 16})
from matplotlib.pyplot import figure
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import RepeatVector
from keras.layers import TimeDistributed
In [15]:
from keras.utils.generic_utils import get_custom_objects
from tensorflow.keras.utils import plot_model
In [16]:
import warnings
from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter('ignore', ConvergenceWarning)

Load Data

In [17]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [18]:
cd drive/MyDrive/Stock price prediction/Generated datasets
/content/drive/.shortcut-targets-by-id/1IaGjVBlTspxI2CHSrxfYnaiYvsaG0pHs/Stock price prediction/Generated datasets
In [19]:
df = pd.read_csv("FULL_Data_google_COVID_bull_bear.csv",parse_dates=[0])
df.tail(10)
Out[19]:
Unnamed: 0 Unnamed: 0.1 Unnamed: 0.1.1 Unnamed: 0.1.1.1 Open High Low Close Adj Close Volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp Date search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
1592 1592 1781 1781 1781 150.199997 151.429993 150.059998 150.809998 150.809998 56787900.0 150.565717 148.423811 -1.137777 2.817933 154.059677 142.787944 150.767809 5.009368 93.428749 -0.061228 100.779503 -0.039111 103.599003 -0.022436 2021-11-09 19 112313 1258 0.119141 0.111328 NaN NaN NaN NaN
1593 1593 1782 1782 1782 150.020004 150.130005 147.850006 147.919998 147.919998 65187100.0 150.417145 148.729049 -1.236913 2.144358 153.017766 144.440332 148.869268 4.989888 92.922909 -0.061683 99.694365 -0.039762 101.872301 -0.022657 2021-11-10 19 80301 1470 0.154297 0.109375 NaN NaN NaN NaN
1594 1594 1783 1783 1783 148.960007 149.429993 147.679993 147.869995 147.869995 41000000.0 150.110001 149.060477 -1.165047 1.767475 152.595428 145.525526 148.203086 4.989548 92.416471 -0.062129 98.604584 -0.040391 100.137594 -0.022839 2021-11-11 19 94975 1662 0.102845 0.126915 NaN NaN NaN NaN
1595 1595 1784 1784 1784 148.429993 150.399994 147.479996 149.990005 149.990005 63632600.0 149.895715 149.357144 -0.869308 1.420732 152.198608 146.515681 149.394365 5.003879 91.909483 -0.062566 97.510555 -0.040998 98.396260 -0.022980 2021-11-12 19 55499 797 0.157277 0.080595 NaN NaN NaN NaN
1596 1596 1785 1785 1785 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2021-11-13 19 146529 2505 0.139459 0.083243 NaN NaN NaN NaN
1597 1597 1786 1786 1786 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2021-11-14 19 40964 479 0.151261 0.100840 NaN NaN NaN NaN
1598 1598 1787 1787 1787 150.369995 151.880005 149.429993 150.000000 150.000000 59222800.0 149.758571 149.602859 -0.907641 1.229694 152.062246 147.143471 149.798122 5.003946 91.401994 -0.062993 96.412672 -0.041581 96.649685 -0.023077 2021-11-15 22 30290 148 0.136737 0.109389 NaN NaN NaN NaN
1599 1599 1788 1788 1788 149.940002 151.490005 149.339996 151.000000 151.000000 59256200.0 149.718571 149.814763 -0.791320 1.236243 152.287250 147.342277 150.599374 5.010635 90.894052 -0.063410 95.311334 -0.042140 94.899260 -0.023130 2021-11-16 22 138962 1294 0.135531 0.115385 NaN NaN NaN NaN
1600 1600 1789 1789 1789 151.000000 155.000000 150.990005 153.490005 153.490005 88807000.0 150.154286 150.040002 -0.657719 1.467121 152.974245 147.105759 152.526461 5.027099 90.385704 -0.063817 94.206941 -0.042673 93.146378 -0.023135 2021-11-17 22 87626 1290 0.100870 0.126957 NaN NaN NaN NaN
1601 1601 1790 1790 1790 153.710007 158.669998 153.050003 157.869995 157.869995 137659100.0 151.162857 150.450002 -0.609656 2.267825 154.985653 145.914351 156.088817 5.055417 89.877000 -0.064214 93.099895 -0.043179 91.392433 -0.023090 2021-11-18 22 111404 1637 0.145098 0.121569 NaN NaN NaN NaN
In [23]:
ls
shell-init: error retrieving current directory: getcwd: cannot access parent directories: No such file or directory
'AG Project NB'/
'Akshada Notebooks'/
'Archana - LSTM Hybrid'/
 dataset_final_tech_ind_sentiment_score.csv
 DL_FinalProject_GoogleTrends_Akshada.ipynb
"Experiment NB's"/
'Generated datasets'/
'PLOTS Akshada'/
 Reports/
 results_updated.xlsx
 results.xlsx
'Stock Closing Price Prediction.pptx'
 Stocks/
'Training data'/
In [24]:
cd Archana - LSTM Hybrid/Outputs/full
/content/drive/.shortcut-targets-by-id/1IaGjVBlTspxI2CHSrxfYnaiYvsaG0pHs/Stock price prediction/Archana - LSTM Hybrid/Outputs/full
In [25]:
pd.to_datetime(df[np.isnan(df.Close)==True]['Date']).dt.day_name().head(5)
Out[25]:
0    Saturday
1      Sunday
3     Tuesday
7    Saturday
8      Sunday
Name: Date, dtype: object
In [26]:
len(pd.to_datetime(df[np.isnan(df.Close)==True]['Date']).dt.day_name())
Out[26]:
497
In [27]:
len(df)
Out[27]:
1602
In [28]:
len(df) - len(pd.to_datetime(df[np.isnan(df.Close)==True]['Date']).dt.day_name())
Out[28]:
1105
In [29]:
df.dropna(inplace=True)
len(df)
Out[29]:
1080
In [30]:
pd.to_datetime(df[np.isnan(df.Close)==True]['Date']).dt.day_name()
Out[30]:
Series([], Name: Date, dtype: object)
In [31]:
df.head(5)
Out[31]:
Unnamed: 0 Unnamed: 0.1 Unnamed: 0.1.1 Unnamed: 0.1.1.1 Open High Low Close Adj Close Volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp Date search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
2 2 191 191 191 36.220001 36.325001 35.775002 35.875000 34.054882 57111200.0 36.173571 36.751904 0.303356 0.960520 38.672945 34.830864 35.924548 3.551770 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 2017-07-03 15 0 0 0.666667 0.000000 0.142778 0.146810 0.100537 0.099251
4 4 193 193 193 35.922501 36.197498 35.680000 36.022499 34.194897 86278400.0 36.095357 36.634762 0.328795 0.852735 38.340231 34.929292 35.989849 3.555991 38.240991 0.049445 29.954520 0.099254 43.438321 -0.053936 2017-07-05 15 0 0 0.400000 0.000000 0.144487 0.145833 0.100630 0.096361
5 5 194 194 194 35.755001 35.875000 35.602501 35.682499 33.872143 96515200.0 35.984999 36.495238 0.346702 0.677629 37.850495 35.139980 35.784949 3.546235 38.027974 0.051918 30.209839 0.095602 43.557403 -0.053820 2017-07-06 15 0 0 0.142857 0.142857 0.145346 0.145164 0.100672 0.094761
6 6 195 195 195 35.724998 36.187500 35.724998 36.044998 34.216255 76806800.0 36.001071 36.362023 0.387422 0.387634 37.137291 35.586756 35.958315 3.556633 37.818962 0.054401 30.470232 0.091907 43.662260 -0.053608 2017-07-07 15 0 0 0.333333 0.000000 0.146208 0.144377 0.100711 0.093072
9 9 198 198 198 36.027500 36.487499 35.842499 36.264999 34.425095 84362400.0 35.973571 36.243809 0.388315 0.308042 36.859893 35.627725 36.162771 3.562891 37.613953 0.056893 30.735430 0.088177 43.752965 -0.053302 2017-07-10 14 0 0 0.000000 0.000000 0.148802 0.141354 0.100808 0.087587
In [32]:
stock_col= list(df.columns)
stock_col = stock_col[4:len(stock_col)]
In [36]:
dataset_final = df[stock_col]
In [37]:
dataset_final.head(5)
Out[37]:
Open High Low Close Adj Close Volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp Date search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
2 36.220001 36.325001 35.775002 35.875000 34.054882 57111200.0 36.173571 36.751904 0.303356 0.960520 38.672945 34.830864 35.924548 3.551770 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 2017-07-03 15 0 0 0.666667 0.000000 0.142778 0.146810 0.100537 0.099251
4 35.922501 36.197498 35.680000 36.022499 34.194897 86278400.0 36.095357 36.634762 0.328795 0.852735 38.340231 34.929292 35.989849 3.555991 38.240991 0.049445 29.954520 0.099254 43.438321 -0.053936 2017-07-05 15 0 0 0.400000 0.000000 0.144487 0.145833 0.100630 0.096361
5 35.755001 35.875000 35.602501 35.682499 33.872143 96515200.0 35.984999 36.495238 0.346702 0.677629 37.850495 35.139980 35.784949 3.546235 38.027974 0.051918 30.209839 0.095602 43.557403 -0.053820 2017-07-06 15 0 0 0.142857 0.142857 0.145346 0.145164 0.100672 0.094761
6 35.724998 36.187500 35.724998 36.044998 34.216255 76806800.0 36.001071 36.362023 0.387422 0.387634 37.137291 35.586756 35.958315 3.556633 37.818962 0.054401 30.470232 0.091907 43.662260 -0.053608 2017-07-07 15 0 0 0.333333 0.000000 0.146208 0.144377 0.100711 0.093072
9 36.027500 36.487499 35.842499 36.264999 34.425095 84362400.0 35.973571 36.243809 0.388315 0.308042 36.859893 35.627725 36.162771 3.562891 37.613953 0.056893 30.735430 0.088177 43.752965 -0.053302 2017-07-10 14 0 0 0.000000 0.000000 0.148802 0.141354 0.100808 0.087587

Data Load for Experiments with Fulldata

In [38]:
# stock_col= list(df.columns)
# stock_col1 = stock_col[4:len(stock_col)-9]
# stock_col2 = stock_col[len(stock_col)-7:len(stock_col)]
# stock_col1.append(stock_col2)
# dataset_final = df
dataset_final.head(5)
Out[38]:
Open High Low Close Adj Close Volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp Date search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
2 36.220001 36.325001 35.775002 35.875000 34.054882 57111200.0 36.173571 36.751904 0.303356 0.960520 38.672945 34.830864 35.924548 3.551770 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 2017-07-03 15 0 0 0.666667 0.000000 0.142778 0.146810 0.100537 0.099251
4 35.922501 36.197498 35.680000 36.022499 34.194897 86278400.0 36.095357 36.634762 0.328795 0.852735 38.340231 34.929292 35.989849 3.555991 38.240991 0.049445 29.954520 0.099254 43.438321 -0.053936 2017-07-05 15 0 0 0.400000 0.000000 0.144487 0.145833 0.100630 0.096361
5 35.755001 35.875000 35.602501 35.682499 33.872143 96515200.0 35.984999 36.495238 0.346702 0.677629 37.850495 35.139980 35.784949 3.546235 38.027974 0.051918 30.209839 0.095602 43.557403 -0.053820 2017-07-06 15 0 0 0.142857 0.142857 0.145346 0.145164 0.100672 0.094761
6 35.724998 36.187500 35.724998 36.044998 34.216255 76806800.0 36.001071 36.362023 0.387422 0.387634 37.137291 35.586756 35.958315 3.556633 37.818962 0.054401 30.470232 0.091907 43.662260 -0.053608 2017-07-07 15 0 0 0.333333 0.000000 0.146208 0.144377 0.100711 0.093072
9 36.027500 36.487499 35.842499 36.264999 34.425095 84362400.0 35.973571 36.243809 0.388315 0.308042 36.859893 35.627725 36.162771 3.562891 37.613953 0.056893 30.735430 0.088177 43.752965 -0.053302 2017-07-10 14 0 0 0.000000 0.000000 0.148802 0.141354 0.100808 0.087587
In [39]:
# Set the date to datetime data
datetime_series = pd.to_datetime(dataset_final['Date'])
datetime_index = pd.DatetimeIndex(datetime_series.values)
dataset_final = dataset_final.set_index(datetime_index)
dataset_final = dataset_final.sort_values(by='Date')
dataset_final = dataset_final.drop(columns='Date')
dataset_final.head(5)
Out[39]:
Open High Low Close Adj Close Volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
2017-07-03 36.220001 36.325001 35.775002 35.875000 34.054882 57111200.0 36.173571 36.751904 0.303356 0.960520 38.672945 34.830864 35.924548 3.551770 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 15 0 0 0.666667 0.000000 0.142778 0.146810 0.100537 0.099251
2017-07-05 35.922501 36.197498 35.680000 36.022499 34.194897 86278400.0 36.095357 36.634762 0.328795 0.852735 38.340231 34.929292 35.989849 3.555991 38.240991 0.049445 29.954520 0.099254 43.438321 -0.053936 15 0 0 0.400000 0.000000 0.144487 0.145833 0.100630 0.096361
2017-07-06 35.755001 35.875000 35.602501 35.682499 33.872143 96515200.0 35.984999 36.495238 0.346702 0.677629 37.850495 35.139980 35.784949 3.546235 38.027974 0.051918 30.209839 0.095602 43.557403 -0.053820 15 0 0 0.142857 0.142857 0.145346 0.145164 0.100672 0.094761
2017-07-07 35.724998 36.187500 35.724998 36.044998 34.216255 76806800.0 36.001071 36.362023 0.387422 0.387634 37.137291 35.586756 35.958315 3.556633 37.818962 0.054401 30.470232 0.091907 43.662260 -0.053608 15 0 0 0.333333 0.000000 0.146208 0.144377 0.100711 0.093072
2017-07-10 36.027500 36.487499 35.842499 36.264999 34.425095 84362400.0 35.973571 36.243809 0.388315 0.308042 36.859893 35.627725 36.162771 3.562891 37.613953 0.056893 30.735430 0.088177 43.752965 -0.053302 14 0 0 0.000000 0.000000 0.148802 0.141354 0.100808 0.087587

Train & test Dataset for Multistep Process

In [41]:
# Get features and target
X_value = pd.DataFrame(dataset_final.iloc[:, :])
y_value = pd.DataFrame(dataset_final.iloc[:, 3])
In [42]:
y_value.head(5)
Out[42]:
Close
2017-07-03 35.875000
2017-07-05 36.022499
2017-07-06 35.682499
2017-07-07 36.044998
2017-07-10 36.264999
In [43]:
# Normalized the data
X_scaler = MinMaxScaler(feature_range=(-1, 1))
y_scaler = MinMaxScaler(feature_range=(-1, 1))
X_scaler.fit(X_value)
y_scaler.fit(y_value)
Out[43]:
MinMaxScaler(feature_range=(-1, 1))
In [44]:
X_scale_dataset = X_scaler.fit_transform(X_value)
y_scale_dataset = y_scaler.fit_transform(y_value)
In [45]:
X_scale_dataset.shape, y_scale_dataset.shape,
Out[45]:
((1080, 29), (1080, 1))
In [46]:
X_value.shape[1]
Out[46]:
29

N Steps Definition

In [47]:
n_steps_in = 3
n_features = X_value.shape[1] #19 features
n_steps_out = 1
In [48]:
# Reshape the data
'''Set the data input steps and output steps, 
    we use 30 days data to predict 1 day price here, 
    reshape it to (None, input_step, number of features) used for LSTM input'''
# Get X/y dataset
def get_X_y(X_data, y_data):
    X = list()
    y = list()
    yc = list()

    length = len(X_data)
    for i in range(0, length, 1):
        # pdb.set_trace()
        X_value = X_data[i: i + n_steps_in][:, :]
        # print('[',i,': ',i,' + ',n_steps_in,'][:, :]')
        y_value = y_data[i + n_steps_in: i + (n_steps_in + n_steps_out)][:, 0]
        # print('[',i,' + ',n_steps_in,': ',i,' + (',n_steps_in,' + ',n_steps_out,')][:, 0]')
        yc_value = y_data[i: i + n_steps_in][:, :]
        if len(X_value) == 3 and len(y_value) == 1:
            X.append(X_value)
            y.append(y_value)
            yc.append(yc_value)

    return np.array(X), np.array(y), np.array(yc)
In [49]:
# get the train test predict index
def predict_index(dataset, X_train, n_steps_in, n_steps_out):

    # get the predict data (remove the in_steps days)
    train_predict_index = dataset.iloc[n_steps_in : X_train.shape[0] + n_steps_in + n_steps_out - 1, :].index
    test_predict_index = dataset.iloc[X_train.shape[0] + n_steps_in:, :].index

    return train_predict_index, test_predict_index
In [50]:
def mean_absolute_percentage_error(actual, prediction):
    actual = pd.Series(actual)
    prediction = pd.Series(prediction)
    return 100 * np.mean(np.abs((actual - prediction))/actual)
In [51]:
# Split train/test dataset
def split_train_test(data):
    train_size = round(len(X) * 0.75)
    data_train = data[0:train_size]
    data_test = data[train_size:]
    return data_train, data_test
In [52]:
# Get data and check shape
X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
# pdb.set_trace()
X_train, X_test, = split_train_test(X)
y_train, y_test, = split_train_test(y)
yc_train, yc_test, = split_train_test(yc)
index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
In [53]:
# %% --------------------------------------- Save dataset -----------------------------------------------------------------
print('X shape: ', X.shape)
print('y shape: ', y.shape)
print('X_train shape: ', X_train.shape)
print('y_train shape: ', y_train.shape)
print('y_c_train shape: ', yc_train.shape)
print('X_test shape: ', X_test.shape)
print('y_test shape: ', y_test.shape)
print('y_c_test shape: ', yc_test.shape)
print('index_train shape:', index_train.shape)
print('index_test shape:', index_test.shape)
X shape:  (1077, 3, 29)
y shape:  (1077, 1)
X_train shape:  (808, 3, 29)
y_train shape:  (808, 1)
y_c_train shape:  (808, 3, 1)
X_test shape:  (269, 3, 29)
y_test shape:  (269, 1)
y_c_test shape:  (269, 3, 1)
index_train shape: (808,)
index_test shape: (269,)
In [54]:
output_dim = y_train.shape[1]
output_dim
Out[54]:
1
In [55]:
df = dataset_final.copy()
In [56]:
df.rename(columns={'Date':'date','Open':'open','Low':'low','Close':'close','Volume':'volume','High':'high'}, inplace = True)
df.reset_index(drop=True,inplace=True)
In [57]:
df.head(1)
Out[57]:
open high low close Adj Close volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
0 36.220001 36.325001 35.775002 35.875 34.054882 57111200.0 36.173571 36.751904 0.303356 0.96052 38.672945 34.830864 35.924548 3.55177 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 15 0 0 0.666667 0.0 0.142778 0.14681 0.100537 0.099251
In [58]:
# df.drop(['volume', 'MACD','20SD','logmomentum','absolute of 3 comp','angle of 3 comp','absolute of 6 comp','angle of 6 comp','absolute of 9 comp','angle of 9 comp'], axis='columns', inplace=True) # only keep columns that can help as residuals in Arima Hybrid
In [59]:
df.head(1)
Out[59]:
open high low close Adj Close volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp search COVID positiveIncrease COVID deathIncrease bull score bear score fourier bull 10 fourier bull 30 fourier bear 10 fourier bear 30
0 36.220001 36.325001 35.775002 35.875 34.054882 57111200.0 36.173571 36.751904 0.303356 0.96052 38.672945 34.830864 35.924548 3.55177 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 15 0 0 0.666667 0.0 0.142778 0.14681 0.100537 0.099251

Train & Test Length

In [60]:
test_len = len(X_test)
In [61]:
train_len = len(X_train )
In [62]:
test_len, train_len
Out[62]:
(269, 808)

Kurtosis Review

In [63]:
# Initialize moving averages from Ta-Lib, store functions in dictionary
# talib_moving_averages = ['SMA', 'EMA', 'WMA', 'DEMA', 'KAMA', 'MIDPOINT', 'MIDPRICE', 'T3', 'TEMA', 'TRIMA'] remove midprice due to outputbeing univariate
talib_moving_averages = ['SMA', 'EMA', 'WMA', 'DEMA', 'KAMA', 'MIDPOINT',  'T3', 'TEMA', 'TRIMA'] 
functions = {}
for ma in talib_moving_averages:
      functions[ma] = abstract.Function(ma)

    # Determine kurtosis "K" values for MA period 4-30
kurtosis_results = {'period': []}
for i in range(4, 100): # 100
  kurtosis_results['period'].append(i)
  for ma in talib_moving_averages:
              # Run moving average, remove last N days (used later for test data set), trim MA result to last 30 days
              ma_output = functions[ma](df[:-test_len], i).tail(14)
              # Determine kurtosis "K" value
              k = kurtosis(ma_output, fisher=False)
              # add to dictionary
              if ma not in kurtosis_results.keys():
                  kurtosis_results[ma] = []
              kurtosis_results[ma].append(k)

kurtosis_results = pd.DataFrame(kurtosis_results)
kurtosis_results.to_csv('kurtosis_results.csv')
In [64]:
kurtosis_results.head(5)
Out[64]:
period SMA EMA WMA DEMA KAMA MIDPOINT T3 TEMA TRIMA
0 4 2.272452 2.652772 2.896972 3.800351 2.299585 2.171369 1.978458 4.609342 2.411225
1 5 1.839451 2.355815 2.481058 3.327525 1.841282 1.826597 1.640277 4.262302 1.994382
2 6 1.583886 2.159532 2.194320 2.945924 1.536136 1.605787 1.510972 3.878845 1.679710
3 7 1.461290 2.026758 1.990629 2.651927 1.506197 1.558096 1.514015 3.510432 1.486348
4 8 1.447516 1.935302 1.853935 2.429648 1.509566 1.621595 1.601580 3.184123 1.373337

Optimized periods

In [65]:
# Determine period with K closest to 3 +/-5%
optimized_period = {}
# https://pypi.org/project/TA-Lib/ determines the type of moving average to use
# https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.at.html#pandas.DataFrame.at
for ma in talib_moving_averages:
        difference = np.abs(kurtosis_results[ma] - 3)
        df_arimahyb = pd.DataFrame({'difference': difference, 'period': kurtosis_results['period']})
        df_arimahyb = df_arimahyb.sort_values(by=['difference'], ascending=True).reset_index(drop=True)
        if df_arimahyb.at[0, 'difference'] < 3 * 0.05:
            optimized_period[ma] = df_arimahyb.at[0, 'period']
        else:
            print(ma + ' is not viable, best K greater or less than 3 +/-5%')

print('\nOptimized periods:', optimized_period)
TRIMA is not viable, best K greater or less than 3 +/-5%

Optimized periods: {'SMA': 17, 'EMA': 51, 'WMA': 49, 'DEMA': 89, 'KAMA': 18, 'MIDPOINT': 14, 'T3': 19, 'TEMA': 9}
In [66]:
optimized_period
Out[66]:
{'DEMA': 89,
 'EMA': 51,
 'KAMA': 18,
 'MIDPOINT': 14,
 'SMA': 17,
 'T3': 19,
 'TEMA': 9,
 'WMA': 49}

Simulation Keys

In [ ]:
simulation = {}
for ma in optimized_period:
        print(ma)
        print(functions[ma])
        print ( int( optimized_period[ma]))
        # if ma in ['EMA','WMA','DEMA','KAMA','MIDPOINT']:
        #   print(ma)
        low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
        low_vol = low_vol.fillna(0)
        high_vol = pd.DataFrame()
        df2 = df.copy()
        for i in df2.columns:
          if i in low_vol.columns:
            high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9
In [ ]:
low_vol.tail(20)
Out[ ]:
open high low close Adj Close volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp search
1060 140.200839 141.942909 138.524500 140.171495 139.966842 8.852448e+07 142.165478 146.699207 1.815578 4.572948 155.845103 137.553312 140.365562 4.935800 105.739092 -0.047411 125.318767 -0.018291 140.471430 -0.008749 19.573385
1061 139.425914 141.705469 138.035200 140.698014 140.492650 8.620711e+07 141.528981 145.978836 2.115887 4.189393 154.357621 137.600050 140.587196 4.939545 105.263514 -0.048037 124.464999 -0.019222 139.335869 -0.009472 19.632022
1062 140.773058 142.636405 139.932338 141.733666 141.526843 7.421445e+07 141.294887 145.298477 2.211018 3.647690 152.593858 138.003097 141.351509 4.946870 104.786174 -0.048658 123.598217 -0.020150 138.164839 -0.010188 19.698090
1063 142.179695 143.266994 141.127848 142.249061 142.041527 6.519616e+07 141.224295 144.665584 2.093072 3.241276 151.148137 138.183031 141.949877 4.950518 104.307114 -0.049275 122.718682 -0.021074 136.959041 -0.010898 19.763505
1064 142.253947 144.008334 141.546689 142.555532 142.347589 6.254214e+07 141.336839 144.184381 1.988881 2.884864 149.954110 138.414652 142.353647 4.952685 103.826381 -0.049886 121.826667 -0.021994 135.719217 -0.011600 20.311676
1065 142.782738 143.732491 141.438660 142.125353 141.918068 6.542511e+07 141.385297 143.758659 1.774804 2.626682 149.012024 138.505294 142.201451 4.949632 103.344020 -0.050491 120.922446 -0.022909 134.446150 -0.012293 20.671514
1066 142.153085 142.656915 140.466684 141.564232 141.357788 7.040262e+07 141.585336 143.387397 1.634667 2.376817 148.141030 138.633764 141.776638 4.945637 102.860075 -0.051092 120.006305 -0.023818 133.140665 -0.012977 20.900131
1067 142.177201 143.194327 140.977156 142.610382 142.402435 6.948112e+07 141.933749 143.094536 1.573317 2.074153 147.242842 138.946230 142.332468 4.953023 102.374593 -0.051687 119.078535 -0.024722 131.803627 -0.013650 21.038585
1068 143.009006 144.052615 142.286776 143.812497 143.602819 6.805244e+07 142.378675 142.879716 1.473333 1.874158 146.628032 139.131400 143.319154 4.961467 101.887619 -0.052275 118.139433 -0.025618 130.435938 -0.014311 21.116168
1069 143.380322 145.547752 142.940349 145.397429 145.185452 7.592729e+07 142.902069 142.813890 1.447641 1.844159 146.502207 139.125573 144.704671 4.972505 101.399198 -0.052858 117.189304 -0.026508 129.038540 -0.014959 21.153587
1070 145.337970 147.615882 144.980528 147.444584 147.229635 7.653090e+07 143.644287 142.961273 1.284466 2.010227 146.981728 138.940819 146.531280 4.986604 100.909377 -0.053435 116.228458 -0.027389 127.612408 -0.015592 21.165321
1071 147.375283 149.163050 146.995423 148.921380 148.704294 6.811986e+07 144.553694 143.236380 0.961952 2.270386 147.777152 138.695607 148.124680 4.996737 100.418203 -0.054006 115.257214 -0.028261 126.158555 -0.016211 21.161363
1072 148.656821 150.010875 148.071943 149.870634 149.652170 6.425222e+07 145.660163 143.530869 0.589081 2.556352 148.643574 138.418164 149.288649 5.003230 99.925720 -0.054570 114.275894 -0.029124 124.678027 -0.016812 21.148490
1073 149.806550 150.715254 149.026204 149.977942 149.759331 6.069918e+07 146.862121 143.785380 0.135134 2.805932 149.397244 138.173516 149.748178 5.003989 99.431976 -0.055128 113.284828 -0.029977 123.171903 -0.017396 21.131204
1074 149.937482 150.666013 149.022091 149.911667 149.693162 5.465321e+07 147.905162 144.001463 -0.245163 3.045742 150.092948 137.909978 149.857170 5.003545 98.937018 -0.055679 112.284350 -0.030820 121.641290 -0.017961 25.016406
1075 150.228161 151.254072 149.586503 150.104281 149.885502 5.602702e+07 148.803988 144.237215 -0.571069 3.270011 150.777237 137.697192 150.021910 5.004835 98.440892 -0.056223 111.274800 -0.031650 120.087330 -0.018506 27.455491
1076 150.328251 150.997797 149.591175 149.912656 149.694163 5.484778e+07 149.449021 144.548659 -0.850904 3.458615 151.465890 137.631428 149.949074 5.003520 97.943645 -0.056759 110.256524 -0.032469 118.511190 -0.019029 28.912854
1077 150.525566 152.430694 150.099878 151.531571 151.310718 7.580033e+07 150.032876 144.967153 -0.975625 3.719924 152.407001 137.527305 151.004072 5.014296 97.445324 -0.057289 109.229873 -0.033274 116.914063 -0.019528 29.716707
1078 149.301052 151.688142 148.723104 151.137179 150.916905 1.012990e+08 150.349418 145.413317 -0.891585 3.905336 153.223988 137.602646 151.092810 5.011652 96.945977 -0.057811 108.195203 -0.034066 115.297171 -0.020004 30.096629
1079 149.321425 151.018197 148.455004 150.396057 150.176865 9.262134e+07 150.424479 145.823313 -0.852689 3.878291 153.579894 138.066731 150.628308 5.006660 96.445650 -0.058325 107.152874 -0.034844 113.661756 -0.020453 27.283213
In [ ]:
high_vol.head(10)
Out[ ]:
open high low close Adj Close volume MA7 MA21 MACD 20SD upper_band lower_band EMA logmomentum absolute of 3 comp angle of 3 comp absolute of 6 comp angle of 6 comp absolute of 9 comp angle of 9 comp search
0 36.220001 36.325001 35.775002 35.875000 34.054882 57111200.0 36.173571 36.751904 0.303356 0.960520 38.672945 34.830864 35.924548 3.551770 38.458011 0.046984 29.704545 0.102857 43.304973 -0.053955 15.0
1 35.922501 36.197498 35.680000 36.022499 34.194897 86278400.0 36.095357 36.634762 0.328795 0.852735 38.340231 34.929292 35.989849 3.555991 38.240991 0.049445 29.954520 0.099254 43.438321 -0.053936 15.0
2 35.755001 35.875000 35.602501 35.682499 33.872143 96515200.0 35.984999 36.495238 0.346702 0.677629 37.850495 35.139980 35.784949 3.546235 38.027974 0.051918 30.209839 0.095602 43.557403 -0.053820 15.0
3 35.724998 36.187500 35.724998 36.044998 34.216255 76806800.0 36.001071 36.362023 0.387422 0.387634 37.137291 35.586756 35.958315 3.556633 37.818962 0.054401 30.470232 0.091907 43.662260 -0.053608 15.0
4 36.027500 36.487499 35.842499 36.264999 34.425095 84362400.0 35.973571 36.243809 0.388315 0.308042 36.859893 35.627725 36.162771 3.562891 37.613953 0.056893 30.735430 0.088177 43.752965 -0.053302 14.0
5 36.182499 36.462502 36.095001 36.382500 34.536625 79127200.0 36.039642 36.202738 0.372153 0.308860 36.820458 35.585018 36.309257 3.566217 37.412947 0.059392 31.005161 0.084416 43.829622 -0.052901 14.0
6 36.467499 36.544998 36.205002 36.435001 34.586472 99538000.0 36.101071 36.206547 0.317572 0.295861 36.798268 35.614826 36.393086 3.567700 37.215939 0.061899 31.279154 0.080632 43.892360 -0.052406 14.0
7 36.375000 37.122501 36.360001 36.942501 35.068211 100797600.0 36.253571 36.220595 0.322643 0.340687 36.901969 35.539221 36.759363 3.581920 37.022928 0.064410 31.557136 0.076830 43.941338 -0.051818 14.0
8 36.992500 37.332500 36.832500 37.259998 35.369610 80528400.0 36.430357 36.266785 0.257925 0.410484 37.087753 35.445818 37.093120 3.590715 36.833908 0.066926 31.838833 0.073014 43.976744 -0.051137 14.0
9 37.205002 37.724998 37.142502 37.389999 35.493000 95174000.0 36.674285 36.329523 0.184267 0.445597 37.220717 35.438330 37.291039 3.594294 36.648875 0.069445 32.123972 0.069192 43.998789 -0.050365 16.0

Common Functions

In [67]:
def get_arima(dataframe,original_data, train_len, test_len):
    # prepare train and test data
    X_value = pd.DataFrame(dataframe.iloc[:, :])
    y_value = pd.DataFrame(dataframe.iloc[:, 3])
    X_train, X_test = split_train_test(X_value)
    y_train, y_test = split_train_test(y_value)
    yc_train,yc_test = split_train_test(original_data)
    # y_train_ = y_train['close'].to_list()
    # y_test_ = y_test['close'].to_list()
    yc = yc_test.values.tolist()
    y_train_list = y_train['close'].values.tolist() 
    y_test_list = y_test['close'].values.tolist()                                           
      
    # Initialize model
    model = auto_arima(y_train_list,trace=True, error_action='ignore', start_p=1,start_q=1,max_p=3,max_q=3,d=3,
                  suppress_warnings=True,stepwise=True,seasonal=True)
    print(model.summary())
        # Determine model parameters
    model.fit(y_train_list,disp= 0)
    order = model.get_params()['order']
    print('ARIMA order:', order, '\n')

        # Genereate predictions
    prediction = []
    for i in range(len(y_test_list)):
            model = pmdarima.ARIMA(order=order)
            model.fit(y_train_list,disp= 0)
            # print('working on', i+1, 'of', len(y_test), '-- ' + str(int(100 * (i + 1) / len(y_test))) + '% complete')
            prediction.append(model.predict()[0])
            y_train_list.append(y_test_list[i])

    # Generate error data
    mse = mean_squared_error(yc_test, prediction)
    rmse = mse ** 0.5
    # mape = mean_absolute_percentage_error(pd.Series(yc_test).values.tolist(), pd.Series(predictionte).values.tolist() )
    mae = mean_absolute_error(pd.Series(yc_test).values.tolist() , pd.Series(prediction).values.tolist() )
    return yc, prediction, mse, rmse, mae
In [68]:
def plot_train(simulation,SIM):
  train_predict_index = np.load("index_train_appl.npy", allow_pickle=True)#Dates for train data

  predict_result = pd.DataFrame()
  for i in range(len(simulation[SIM]['final_tr']['prediction'])):
          y_predict = pd.DataFrame(simulation[SIM]['final_tr']['prediction'][i], columns=["predicted_price"],
                                  index=train_predict_index[i:i + output_dim])
          predict_result = pd.concat([predict_result, y_predict], axis=1, sort=False)
          
          #This is a dataframe with each column containing the predicted daily closing price
  real_price = pd.DataFrame()
  for i in range(len(simulation[SIM]['final_tr']['original'])):
          y_train = pd.DataFrame(simulation[SIM]['final_tr']['original'][i], columns=["real_price"],
                                index=train_predict_index[i:i + output_dim])
          real_price = pd.concat([real_price, y_train], axis=1, sort=False)  #This is a dataframe with each column containing the real daily closing price

  predict_result['predicted_mean'] = predict_result.mean(axis=1)#Adding a column with the daily predicted closing price value
  real_price['real_mean'] = real_price.mean(axis=1)#Adding a column with the daily real closing price value
      #
      # Plot the predicted result
  plt.figure(figsize=(16, 8))
  plt.plot(real_price["real_mean"])
  plt.plot(predict_result["predicted_mean"], color='r')
  plt.xlabel("Date")
  plt.ylabel("Stock price")
  plt.legend(("Real price", "Predicted price"), loc="upper left", fontsize=16)
  plt.title(f"The result of Training for {SIM}", fontsize=20)
  plt.show()

      # Calculate RMSE
  predicted = predict_result["predicted_mean"]
  real = real_price["real_mean"]
  RMSE = np.sqrt(mean_squared_error(predicted, real))
  MSE = mean_squared_error(predicted, real)
  MAE = mean_absolute_error(predicted, real)
  print(f"----- Train RMSE for {SIM} -----", RMSE)
  print(f"----- Train_MSE_LSTM for {SIM} -----", MSE)
  print(f"----- Train MAE LSTM for {SIM} -----", MAE)
In [69]:
def plot_test(simulation, SIM):
  test_predict_index = np.load("index_test_appl.npy", allow_pickle=True)#Dates for train data

      # rescaled_real_y = y_scaler.inverse_transform(y_train)#Real closing price data
      # rescaled_predicted_y = y_scaler.inverse_transform(train_yhat)#Predicted closing price data

  predict_result = pd.DataFrame()
  for i in range(len(simulation[SIM]['final']['prediction'])):
          y_predict = pd.DataFrame(simulation[SIM]['final']['prediction'][i], columns=["predicted_price"],
                                  index=test_predict_index[i:i + output_dim])
          predict_result = pd.concat([predict_result, y_predict], axis=1, sort=False)#This is a dataframe with each column containing the predicted daily closing price
      #
  real_price = pd.DataFrame()
  for i in range(len(simulation[SIM]['final']['original'])):
          y_train = pd.DataFrame(simulation[SIM]['final']['original'][i], columns=["real_price"],
                                index=test_predict_index[i:i + output_dim])
          real_price = pd.concat([real_price, y_train], axis=1, sort=False)#This is a dataframe with each column containing the real daily closing price

  predict_result['predicted_mean'] = predict_result.mean(axis=1)#Adding a column with the daily predicted closing price value
  real_price['real_mean'] = real_price.mean(axis=1)#Adding a column with the daily real closing price value
      #
      # Plot the predicted result
  plt.figure(figsize=(16, 8))
  plt.plot(real_price["real_mean"])
  plt.plot(predict_result["predicted_mean"], color='r')
  plt.xlabel("Date")
  plt.ylabel("Stock price")
  plt.legend(("Real price", "Predicted price"), loc="upper left", fontsize=16)
  plt.title(f"The result of Testing for {SIM}", fontsize=20)
  plt.show()

      # Calculate RMSE
  predicted = predict_result["predicted_mean"]
  real = real_price["real_mean"]
  RMSE = np.sqrt(mean_squared_error(predicted, real))
  MSE = mean_squared_error(predicted, real)
  MAE = mean_absolute_error(predicted, real)
  print(f"----- Test RMSE for {SIM}-----", RMSE)
  print(f"----- Test_MSE_LSTM for {SIM}-----", MSE)
  print(f"----- Test_MAE_LSTM for {SIM}-----", MAE)
In [70]:
def plot_train_high(simulation, SIM):
  train_predict_index = np.load("index_test_appl.npy", allow_pickle=True)#Dates for train data

  predict_result = pd.DataFrame()
  for i in range(len(simulation[SIM]['high_vol']['prediction'])):
          y_predict = pd.DataFrame(simulation[SIM]['high_vol']['prediction'][i], columns=["predicted_price"],
                                  index=train_predict_index[i:i + output_dim])
          predict_result = pd.concat([predict_result, y_predict], axis=1, sort=False)
          
          #This is a dataframe with each column containing the predicted daily closing price
  real_price = pd.DataFrame()
  for i in range(len(simulation[SIM]['high_vol']['original'])):
          y_train = pd.DataFrame(simulation[SIM]['high_vol']['original'][i], columns=["real_price"],
                                index=train_predict_index[i:i + output_dim])
          real_price = pd.concat([real_price, y_train], axis=1, sort=False)  #This is a dataframe with each column containing the real daily closing price

  predict_result['predicted_mean'] = predict_result.mean(axis=1)#Adding a column with the daily predicted closing price value
  real_price['real_mean'] = real_price.mean(axis=1)#Adding a column with the daily real closing price value
      #
      # Plot the predicted result
  plt.figure(figsize=(16, 8))
  plt.plot(real_price["real_mean"])
  plt.plot(predict_result["predicted_mean"], color='r')
  plt.xlabel("Date")
  plt.ylabel("Stock price")
  plt.legend(("Real price", "Predicted price"), loc="upper left", fontsize=16)
  plt.title(f"The result of Training for {SIM}", fontsize=20)
  plt.show()

      # Calculate RMSE
  predicted = predict_result["predicted_mean"]
  real = real_price["real_mean"]
  RMSE = np.sqrt(mean_squared_error(predicted, real))
  MSE = mean_squared_error(predicted, real)
  MAE = mean_absolute_error(predicted, real)
  print(f"----- Individual LSTM RMSE for {SIM} -----", RMSE)
  print(f"----- Individual LSTM_MSE_LSTM for {SIM} -----", MSE)
  print(f"----- Individual LSTM MAE LSTM for {SIM} -----", MAE)
In [71]:
def plot_train_low(simulation , SIM):
  train_predict_index = np.load("index_test_appl.npy", allow_pickle=True)#Dates for train data

  predict_result = pd.DataFrame()
  for i in range(len(simulation[SIM]['low_vol']['prediction'])):
          y_predict = pd.DataFrame(simulation[SIM]['low_vol']['prediction'][i], columns=["predicted_price"],
                                  index=train_predict_index[i:i + output_dim])
          predict_result = pd.concat([predict_result, y_predict], axis=1, sort=False)
          
          #This is a dataframe with each column containing the predicted daily closing price
  real_price = pd.DataFrame()
  for i in range(len(simulation[SIM]['low_vol']['original'])):
          y_train = pd.DataFrame(simulation[SIM]['low_vol']['original'][i], columns=["real_price"],
                                index=train_predict_index[i:i + output_dim])
          real_price = pd.concat([real_price, y_train], axis=1, sort=False)  #This is a dataframe with each column containing the real daily closing price

  predict_result['predicted_mean'] = predict_result.mean(axis=1)#Adding a column with the daily predicted closing price value
  real_price['real_mean'] = real_price.mean(axis=1)#Adding a column with the daily real closing price value
      #
      # Plot the predicted result
  plt.figure(figsize=(16, 8))
  plt.plot(real_price["real_mean"])
  plt.plot(predict_result["predicted_mean"], color='r')
  plt.xlabel("Date")
  plt.ylabel("Stock price")
  plt.legend(("Real price", "Predicted price"), loc="upper left", fontsize=16)
  plt.title(f"The result of Training for {SIM}", fontsize=20)
  plt.show()

      # Calculate RMSE
  predicted = predict_result["predicted_mean"]
  real = real_price["real_mean"]
  RMSE = np.sqrt(mean_squared_error(predicted, real))
  MSE = mean_squared_error(predicted, real)
  MAE = mean_absolute_error(predicted, real)
  print(f"-----Arima RMSE for {SIM} -----", RMSE)
  print(f"----- Arima MSE for {SIM} -----", MSE)
  print(f"----- Arima MAE for {SIM} -----", MAE)
In [72]:
import os
os.getcwd()
Out[72]:
'/content/drive/.shortcut-targets-by-id/1IaGjVBlTspxI2CHSrxfYnaiYvsaG0pHs/Stock price prediction/Archana - LSTM Hybrid/Outputs/full'

Univariate Arima Multistep MutiVariate LSTM Hybrid Model Experiment 1

In [102]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det = 20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # Option 1
    # Set up & fit LSTM RNN
    model = Sequential()
    model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    model.add(Dense(units=64,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(units=output_dim))
    model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    ## Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()


    # # option 2
    # model = Sequential()
    # model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    # model.add(Dense(64))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    # class Double_Tanh(Activation):
    #     def __init__(self, activation, **kwargs):
    #         super(Double_Tanh, self).__init__(activation, **kwargs)
    #         self.__name__ = 'double_tanh'

    # def double_tanh(x):
    #     return (K.tanh(x) * 2)

    # get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
    #     # Model Generation
    # model = Sequential()
    # #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    # model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    # model.add(Dense(1))
    # model.add(Activation(double_tanh))
    # model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(x_train.shape[1], 1)))
    # model.add(LSTM(units=int(lstm_len/2)))
    # model.add(Dense(1, activation='sigmoid'))
    # model.compile(loss='mean_squared_error', optimizer='adam')
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [103]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation1 = {}
    imgfile = 'Experiment1'
    for ma in optimized_period:
              print(ma)
              print(functions[ma])
              print ( int( optimized_period[ma]))
            # if ma == 'SMA':
              low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
              low_vol = low_vol.fillna(0)
              low_vol_data = df['close']
              high_vol = pd.DataFrame()
              df2 = df.copy()
              for i in df2.columns:
                if i in low_vol.columns:
                  high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
              high_vol_data = df['close']
              ## *****************************************************
              # Generate ARIMA and LSTM predictions
              print('\nWorking on ' + ma + ' predictions')
              try:
                print('parameters used : ', train_len, test_len)
                low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima(low_vol,low_vol_data, train_len, test_len)
              except:
                  print('ARIMA error, skipping to next MA type')
                  continue
              Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
              final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
              mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
              rmse_ftr = mse_ftr ** 0.5
              mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
              mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

              final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
              mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
              rmse = mse ** 0.5
              mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              # Generate prediction accuracy
              actual = df['close'].tail(test_len).values
              result_1 = []
              result_2 = []
              for i in range(1, len(final_prediction)):
                  # Compare prediction to previous close price
                  if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                      result_1.append(1)
                  elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                      result_1.append(1)
                  else:
                      result_1.append(0)

                  # Compare prediction to previous prediction
                  if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                      result_2.append(1)
                  elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                      result_2.append(1)
                  else:
                      result_2.append(0)

              accuracy_1 = np.mean(result_1)
              accuracy_2 = np.mean(result_2)

              simulation1[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                            'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                            'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                            'rmse': rmse_ftr, 'mae' : mae_ftr},
                                'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                          'rmse': rmse, 'mae': mae },
                                'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

              # save simulation data here as checkpoint
              with open('simulation1_data.json', 'w') as fp:
                  json.dump(simulation1, fp)

              for ma in simulation1.keys():
                  print('\n' + ma)
                  print('Prediction vs Close:\t\t' + str(round(100*simulation1[ma]['accuracy']['prediction vs close'], 2))
                        + '% Accuracy')
                  print('Prediction vs Prediction:\t' + str(round(100*simulation1[ma]['accuracy']['prediction vs prediction'], 2))
                        + '% Accuracy')
                  print('MSE:\t', simulation1[ma]['final']['mse'],
                        '\nRMSE:\t', simulation1[ma]['final']['rmse'],
                        '\nMAPE:\t', simulation1[ma]['final']['mae'])#,
                        # '\nMAPE:\t', simulation[ma]['final']['mape'])
            # else:
            #   break
            # code you want to evaluate
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.48 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4157.020, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3687.148, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.23 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3458.651, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3322.133, Time=0.11 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.74 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.77 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3324.133, Time=0.21 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.697 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1657.067
Date:                Sun, 12 Dec 2021   AIC                           3322.133
Time:                        15:35:45   BIC                           3340.897
Sample:                             0   HQIC                          3329.339
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1966      0.003   -387.226      0.000      -1.203      -1.191
ar.L2         -0.8952      0.006   -138.692      0.000      -0.908      -0.883
ar.L3         -0.3968      0.006    -68.284      0.000      -0.408      -0.385
sigma2         3.5858      0.017    214.535      0.000       3.553       3.619
===================================================================================
Ljung-Box (L1) (Q):                  14.47   Jarque-Bera (JB):           2428881.42
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       271.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_48 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_48 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.49136, saving model to LSTM1.h5
48/48 - 2s - loss: 0.2128 - val_loss: 0.4914 - lr: 0.0010 - 2s/epoch - 40ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.49136 to 0.03252, saving model to LSTM1.h5
48/48 - 0s - loss: 0.1132 - val_loss: 0.0325 - lr: 0.0010 - 474ms/epoch - 10ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0752 - val_loss: 0.4517 - lr: 0.0010 - 409ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0775 - val_loss: 0.1138 - lr: 0.0010 - 405ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0807 - val_loss: 0.2971 - lr: 0.0010 - 484ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0607 - val_loss: 0.1280 - lr: 0.0010 - 392ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0445 - val_loss: 0.2664 - lr: 0.0010 - 438ms/epoch - 9ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0460 - val_loss: 0.2209 - lr: 1.0000e-04 - 431ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0442 - val_loss: 0.1911 - lr: 1.0000e-04 - 415ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0429 - val_loss: 0.1582 - lr: 1.0000e-04 - 392ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0394 - val_loss: 0.1458 - lr: 1.0000e-04 - 431ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00012: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0360 - val_loss: 0.1350 - lr: 1.0000e-04 - 444ms/epoch - 9ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0347 - val_loss: 0.1336 - lr: 1.0000e-05 - 426ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0312 - val_loss: 0.1331 - lr: 1.0000e-05 - 482ms/epoch - 10ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0333 - val_loss: 0.1328 - lr: 1.0000e-05 - 390ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0313 - val_loss: 0.1316 - lr: 1.0000e-05 - 419ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00017: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0361 - val_loss: 0.1306 - lr: 1.0000e-05 - 384ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0350 - val_loss: 0.1300 - lr: 1.0000e-05 - 392ms/epoch - 8ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0374 - val_loss: 0.1300 - lr: 1.0000e-05 - 442ms/epoch - 9ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0332 - val_loss: 0.1293 - lr: 1.0000e-05 - 437ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0343 - val_loss: 0.1283 - lr: 1.0000e-05 - 429ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0321 - val_loss: 0.1297 - lr: 1.0000e-05 - 388ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0327 - val_loss: 0.1286 - lr: 1.0000e-05 - 431ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0354 - val_loss: 0.1280 - lr: 1.0000e-05 - 419ms/epoch - 9ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0300 - val_loss: 0.1262 - lr: 1.0000e-05 - 427ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0371 - val_loss: 0.1251 - lr: 1.0000e-05 - 399ms/epoch - 8ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0310 - val_loss: 0.1259 - lr: 1.0000e-05 - 380ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0346 - val_loss: 0.1260 - lr: 1.0000e-05 - 413ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0319 - val_loss: 0.1261 - lr: 1.0000e-05 - 433ms/epoch - 9ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0355 - val_loss: 0.1244 - lr: 1.0000e-05 - 415ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0349 - val_loss: 0.1222 - lr: 1.0000e-05 - 403ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0334 - val_loss: 0.1220 - lr: 1.0000e-05 - 399ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03252
48/48 - 1s - loss: 0.0329 - val_loss: 0.1206 - lr: 1.0000e-05 - 508ms/epoch - 11ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0303 - val_loss: 0.1205 - lr: 1.0000e-05 - 394ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0347 - val_loss: 0.1183 - lr: 1.0000e-05 - 402ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0317 - val_loss: 0.1193 - lr: 1.0000e-05 - 398ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0334 - val_loss: 0.1187 - lr: 1.0000e-05 - 382ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0332 - val_loss: 0.1186 - lr: 1.0000e-05 - 410ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0330 - val_loss: 0.1176 - lr: 1.0000e-05 - 420ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0339 - val_loss: 0.1172 - lr: 1.0000e-05 - 452ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0320 - val_loss: 0.1174 - lr: 1.0000e-05 - 446ms/epoch - 9ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0300 - val_loss: 0.1173 - lr: 1.0000e-05 - 383ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0358 - val_loss: 0.1162 - lr: 1.0000e-05 - 429ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0315 - val_loss: 0.1137 - lr: 1.0000e-05 - 443ms/epoch - 9ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03252
48/48 - 1s - loss: 0.0318 - val_loss: 0.1128 - lr: 1.0000e-05 - 508ms/epoch - 11ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0355 - val_loss: 0.1126 - lr: 1.0000e-05 - 413ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0350 - val_loss: 0.1122 - lr: 1.0000e-05 - 426ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0328 - val_loss: 0.1127 - lr: 1.0000e-05 - 469ms/epoch - 10ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0336 - val_loss: 0.1123 - lr: 1.0000e-05 - 398ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0347 - val_loss: 0.1106 - lr: 1.0000e-05 - 447ms/epoch - 9ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0322 - val_loss: 0.1083 - lr: 1.0000e-05 - 467ms/epoch - 10ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03252
48/48 - 0s - loss: 0.0330 - val_loss: 0.1067 - lr: 1.0000e-05 - 418ms/epoch - 9ms/step
Epoch 00052: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.43 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4231.556, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3761.238, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.27 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3532.227, Time=0.08 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3394.496, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.88 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.64 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3396.496, Time=0.21 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.681 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1693.248
Date:                Sun, 12 Dec 2021   AIC                           3394.496
Time:                        15:37:18   BIC                           3413.260
Sample:                             0   HQIC                          3401.702
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.569      0.000      -1.204      -1.192
ar.L2         -0.8976      0.006   -139.811      0.000      -0.910      -0.885
ar.L3         -0.3984      0.006    -68.662      0.000      -0.410      -0.387
sigma2         3.9230      0.018    215.372      0.000       3.887       3.959
===================================================================================
Ljung-Box (L1) (Q):                  14.54   Jarque-Bera (JB):           2462173.05
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.82
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_49 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_49 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 1.23130, saving model to LSTM1.h5
16/16 - 2s - loss: 0.1617 - val_loss: 1.2313 - lr: 0.0010 - 2s/epoch - 99ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 1.23130 to 0.03856, saving model to LSTM1.h5
16/16 - 0s - loss: 0.0921 - val_loss: 0.0386 - lr: 0.0010 - 176ms/epoch - 11ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.3979 - val_loss: 0.0917 - lr: 0.0010 - 160ms/epoch - 10ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.1546 - val_loss: 0.1976 - lr: 0.0010 - 147ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0758 - val_loss: 0.1013 - lr: 0.0010 - 143ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0642 - val_loss: 0.0943 - lr: 0.0010 - 158ms/epoch - 10ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0524 - val_loss: 0.0848 - lr: 0.0010 - 162ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0504 - val_loss: 0.0865 - lr: 1.0000e-04 - 140ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0511 - val_loss: 0.0918 - lr: 1.0000e-04 - 153ms/epoch - 10ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0502 - val_loss: 0.0940 - lr: 1.0000e-04 - 154ms/epoch - 10ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0514 - val_loss: 0.0925 - lr: 1.0000e-04 - 152ms/epoch - 10ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00012: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0457 - val_loss: 0.0894 - lr: 1.0000e-04 - 174ms/epoch - 11ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0462 - val_loss: 0.0897 - lr: 1.0000e-05 - 159ms/epoch - 10ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0380 - val_loss: 0.0898 - lr: 1.0000e-05 - 178ms/epoch - 11ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0469 - val_loss: 0.0898 - lr: 1.0000e-05 - 145ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0442 - val_loss: 0.0898 - lr: 1.0000e-05 - 146ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00017: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0457 - val_loss: 0.0899 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0446 - val_loss: 0.0899 - lr: 1.0000e-05 - 158ms/epoch - 10ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0461 - val_loss: 0.0897 - lr: 1.0000e-05 - 187ms/epoch - 12ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0463 - val_loss: 0.0893 - lr: 1.0000e-05 - 156ms/epoch - 10ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0468 - val_loss: 0.0888 - lr: 1.0000e-05 - 180ms/epoch - 11ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0439 - val_loss: 0.0886 - lr: 1.0000e-05 - 151ms/epoch - 9ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0480 - val_loss: 0.0884 - lr: 1.0000e-05 - 161ms/epoch - 10ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0425 - val_loss: 0.0883 - lr: 1.0000e-05 - 162ms/epoch - 10ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0418 - val_loss: 0.0886 - lr: 1.0000e-05 - 168ms/epoch - 10ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0400 - val_loss: 0.0886 - lr: 1.0000e-05 - 150ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0462 - val_loss: 0.0887 - lr: 1.0000e-05 - 162ms/epoch - 10ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0472 - val_loss: 0.0882 - lr: 1.0000e-05 - 173ms/epoch - 11ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0441 - val_loss: 0.0880 - lr: 1.0000e-05 - 145ms/epoch - 9ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0488 - val_loss: 0.0882 - lr: 1.0000e-05 - 171ms/epoch - 11ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0443 - val_loss: 0.0880 - lr: 1.0000e-05 - 178ms/epoch - 11ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0478 - val_loss: 0.0876 - lr: 1.0000e-05 - 143ms/epoch - 9ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0426 - val_loss: 0.0871 - lr: 1.0000e-05 - 161ms/epoch - 10ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0439 - val_loss: 0.0871 - lr: 1.0000e-05 - 148ms/epoch - 9ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0435 - val_loss: 0.0869 - lr: 1.0000e-05 - 162ms/epoch - 10ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0400 - val_loss: 0.0874 - lr: 1.0000e-05 - 152ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0463 - val_loss: 0.0875 - lr: 1.0000e-05 - 165ms/epoch - 10ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0443 - val_loss: 0.0871 - lr: 1.0000e-05 - 147ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0469 - val_loss: 0.0871 - lr: 1.0000e-05 - 144ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0461 - val_loss: 0.0865 - lr: 1.0000e-05 - 143ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0434 - val_loss: 0.0862 - lr: 1.0000e-05 - 142ms/epoch - 9ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0430 - val_loss: 0.0864 - lr: 1.0000e-05 - 153ms/epoch - 10ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0430 - val_loss: 0.0859 - lr: 1.0000e-05 - 156ms/epoch - 10ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0440 - val_loss: 0.0857 - lr: 1.0000e-05 - 164ms/epoch - 10ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0438 - val_loss: 0.0854 - lr: 1.0000e-05 - 140ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0425 - val_loss: 0.0853 - lr: 1.0000e-05 - 142ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0445 - val_loss: 0.0846 - lr: 1.0000e-05 - 154ms/epoch - 10ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0461 - val_loss: 0.0848 - lr: 1.0000e-05 - 155ms/epoch - 10ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0432 - val_loss: 0.0848 - lr: 1.0000e-05 - 153ms/epoch - 10ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0427 - val_loss: 0.0845 - lr: 1.0000e-05 - 157ms/epoch - 10ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0426 - val_loss: 0.0841 - lr: 1.0000e-05 - 179ms/epoch - 11ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03856
16/16 - 0s - loss: 0.0439 - val_loss: 0.0840 - lr: 1.0000e-05 - 138ms/epoch - 9ms/step
Epoch 00052: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.42 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4264.089, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3793.930, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.24 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3564.923, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3427.258, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.29 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.44 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3429.258, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.809 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1709.629
Date:                Sun, 12 Dec 2021   AIC                           3427.258
Time:                        15:38:38   BIC                           3446.021
Sample:                             0   HQIC                          3434.464
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1981      0.003   -389.386      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.699      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.737      0.000      -0.410      -0.387
sigma2         4.0860      0.019    215.311      0.000       4.049       4.123
===================================================================================
Ljung-Box (L1) (Q):                  14.57   Jarque-Bera (JB):           2460901.70
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_50 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_50 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.74867, saving model to LSTM1.h5
17/17 - 2s - loss: 0.4575 - val_loss: 0.7487 - lr: 0.0010 - 2s/epoch - 93ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.74867 to 0.25174, saving model to LSTM1.h5
17/17 - 0s - loss: 0.1118 - val_loss: 0.2517 - lr: 0.0010 - 195ms/epoch - 11ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.25174 to 0.15433, saving model to LSTM1.h5
17/17 - 0s - loss: 0.1083 - val_loss: 0.1543 - lr: 0.0010 - 172ms/epoch - 10ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.15433
17/17 - 0s - loss: 0.1324 - val_loss: 0.1817 - lr: 0.0010 - 162ms/epoch - 10ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.15433 to 0.09425, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0746 - val_loss: 0.0942 - lr: 0.0010 - 174ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.09425 to 0.06911, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0537 - val_loss: 0.0691 - lr: 0.0010 - 177ms/epoch - 10ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.06911
17/17 - 0s - loss: 0.0534 - val_loss: 0.0721 - lr: 0.0010 - 163ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.06911 to 0.05185, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0602 - val_loss: 0.0519 - lr: 0.0010 - 204ms/epoch - 12ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05185
17/17 - 0s - loss: 0.0471 - val_loss: 0.0846 - lr: 0.0010 - 164ms/epoch - 10ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05185
17/17 - 0s - loss: 0.0429 - val_loss: 0.0621 - lr: 0.0010 - 160ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.05185 to 0.05073, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0459 - val_loss: 0.0507 - lr: 0.0010 - 161ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.05073 to 0.04830, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0351 - val_loss: 0.0483 - lr: 0.0010 - 166ms/epoch - 10ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04830
17/17 - 0s - loss: 0.0385 - val_loss: 0.0662 - lr: 0.0010 - 166ms/epoch - 10ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04830
17/17 - 0s - loss: 0.0319 - val_loss: 0.0607 - lr: 0.0010 - 163ms/epoch - 10ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04830
17/17 - 0s - loss: 0.0315 - val_loss: 0.0621 - lr: 0.0010 - 167ms/epoch - 10ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.04830
17/17 - 0s - loss: 0.0306 - val_loss: 0.0662 - lr: 0.0010 - 153ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.04830 to 0.04409, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0321 - val_loss: 0.0441 - lr: 0.0010 - 167ms/epoch - 10ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04409
17/17 - 0s - loss: 0.0254 - val_loss: 0.0749 - lr: 0.0010 - 156ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04409
17/17 - 0s - loss: 0.0323 - val_loss: 0.0527 - lr: 0.0010 - 162ms/epoch - 10ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04409
17/17 - 0s - loss: 0.0318 - val_loss: 0.0809 - lr: 0.0010 - 160ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss improved from 0.04409 to 0.03896, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0275 - val_loss: 0.0390 - lr: 0.0010 - 227ms/epoch - 13ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03896
17/17 - 0s - loss: 0.0314 - val_loss: 0.0715 - lr: 0.0010 - 152ms/epoch - 9ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03896
17/17 - 0s - loss: 0.0339 - val_loss: 0.0443 - lr: 0.0010 - 148ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss improved from 0.03896 to 0.02617, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0369 - val_loss: 0.0262 - lr: 0.0010 - 178ms/epoch - 10ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02617
17/17 - 0s - loss: 0.0455 - val_loss: 0.0532 - lr: 0.0010 - 166ms/epoch - 10ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02617
17/17 - 0s - loss: 0.0427 - val_loss: 0.0415 - lr: 0.0010 - 167ms/epoch - 10ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02617
17/17 - 0s - loss: 0.0466 - val_loss: 0.0819 - lr: 0.0010 - 153ms/epoch - 9ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.02617 to 0.02424, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0300 - val_loss: 0.0242 - lr: 0.0010 - 180ms/epoch - 11ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.02424
17/17 - 0s - loss: 0.0331 - val_loss: 0.0634 - lr: 0.0010 - 173ms/epoch - 10ms/step
Epoch 30/500

Epoch 00030: val_loss improved from 0.02424 to 0.02407, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0271 - val_loss: 0.0241 - lr: 0.0010 - 173ms/epoch - 10ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02407
17/17 - 0s - loss: 0.0243 - val_loss: 0.0297 - lr: 0.0010 - 186ms/epoch - 11ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02407
17/17 - 0s - loss: 0.0235 - val_loss: 0.0328 - lr: 0.0010 - 150ms/epoch - 9ms/step
Epoch 33/500

Epoch 00033: val_loss improved from 0.02407 to 0.02338, saving model to LSTM1.h5
17/17 - 0s - loss: 0.0233 - val_loss: 0.0234 - lr: 0.0010 - 171ms/epoch - 10ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0260 - val_loss: 0.0238 - lr: 0.0010 - 155ms/epoch - 9ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0222 - val_loss: 0.1222 - lr: 0.0010 - 163ms/epoch - 10ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0231 - val_loss: 0.0304 - lr: 0.0010 - 154ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0271 - val_loss: 0.0643 - lr: 0.0010 - 222ms/epoch - 13ms/step
Epoch 38/500

Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00038: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0225 - val_loss: 0.0321 - lr: 0.0010 - 179ms/epoch - 11ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0224 - val_loss: 0.0361 - lr: 1.0000e-04 - 163ms/epoch - 10ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0231 - val_loss: 0.0362 - lr: 1.0000e-04 - 156ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0217 - val_loss: 0.0379 - lr: 1.0000e-04 - 148ms/epoch - 9ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0202 - val_loss: 0.0373 - lr: 1.0000e-04 - 155ms/epoch - 9ms/step
Epoch 43/500

Epoch 00043: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00043: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0205 - val_loss: 0.0361 - lr: 1.0000e-04 - 158ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0211 - val_loss: 0.0360 - lr: 1.0000e-05 - 165ms/epoch - 10ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0207 - val_loss: 0.0358 - lr: 1.0000e-05 - 160ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0193 - val_loss: 0.0357 - lr: 1.0000e-05 - 155ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0204 - val_loss: 0.0357 - lr: 1.0000e-05 - 150ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00048: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0213 - val_loss: 0.0358 - lr: 1.0000e-05 - 155ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0206 - val_loss: 0.0359 - lr: 1.0000e-05 - 166ms/epoch - 10ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0212 - val_loss: 0.0359 - lr: 1.0000e-05 - 159ms/epoch - 9ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0217 - val_loss: 0.0361 - lr: 1.0000e-05 - 166ms/epoch - 10ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0217 - val_loss: 0.0363 - lr: 1.0000e-05 - 176ms/epoch - 10ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0212 - val_loss: 0.0363 - lr: 1.0000e-05 - 190ms/epoch - 11ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0202 - val_loss: 0.0363 - lr: 1.0000e-05 - 161ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0203 - val_loss: 0.0362 - lr: 1.0000e-05 - 158ms/epoch - 9ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0202 - val_loss: 0.0361 - lr: 1.0000e-05 - 170ms/epoch - 10ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0225 - val_loss: 0.0360 - lr: 1.0000e-05 - 158ms/epoch - 9ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0206 - val_loss: 0.0360 - lr: 1.0000e-05 - 163ms/epoch - 10ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0213 - val_loss: 0.0359 - lr: 1.0000e-05 - 152ms/epoch - 9ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0200 - val_loss: 0.0360 - lr: 1.0000e-05 - 159ms/epoch - 9ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0187 - val_loss: 0.0359 - lr: 1.0000e-05 - 151ms/epoch - 9ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0204 - val_loss: 0.0359 - lr: 1.0000e-05 - 170ms/epoch - 10ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0210 - val_loss: 0.0360 - lr: 1.0000e-05 - 183ms/epoch - 11ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0201 - val_loss: 0.0362 - lr: 1.0000e-05 - 180ms/epoch - 11ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0198 - val_loss: 0.0365 - lr: 1.0000e-05 - 189ms/epoch - 11ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0218 - val_loss: 0.0369 - lr: 1.0000e-05 - 161ms/epoch - 9ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0209 - val_loss: 0.0367 - lr: 1.0000e-05 - 170ms/epoch - 10ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0213 - val_loss: 0.0369 - lr: 1.0000e-05 - 164ms/epoch - 10ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0215 - val_loss: 0.0369 - lr: 1.0000e-05 - 170ms/epoch - 10ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0207 - val_loss: 0.0371 - lr: 1.0000e-05 - 154ms/epoch - 9ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0203 - val_loss: 0.0371 - lr: 1.0000e-05 - 148ms/epoch - 9ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0213 - val_loss: 0.0371 - lr: 1.0000e-05 - 150ms/epoch - 9ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0207 - val_loss: 0.0370 - lr: 1.0000e-05 - 156ms/epoch - 9ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0231 - val_loss: 0.0371 - lr: 1.0000e-05 - 173ms/epoch - 10ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0232 - val_loss: 0.0368 - lr: 1.0000e-05 - 158ms/epoch - 9ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0205 - val_loss: 0.0367 - lr: 1.0000e-05 - 161ms/epoch - 9ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0197 - val_loss: 0.0365 - lr: 1.0000e-05 - 167ms/epoch - 10ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0203 - val_loss: 0.0364 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0211 - val_loss: 0.0365 - lr: 1.0000e-05 - 152ms/epoch - 9ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0221 - val_loss: 0.0365 - lr: 1.0000e-05 - 174ms/epoch - 10ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0195 - val_loss: 0.0367 - lr: 1.0000e-05 - 157ms/epoch - 9ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0189 - val_loss: 0.0368 - lr: 1.0000e-05 - 161ms/epoch - 9ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.02338
17/17 - 0s - loss: 0.0204 - val_loss: 0.0367 - lr: 1.0000e-05 - 157ms/epoch - 9ms/step
Epoch 00083: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965

WMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 80.72598349686672 
RMSE:	 8.9847639644493 
MAPE:	 7.266216353433966
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.42 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4436.126, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3965.317, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3736.589, Time=0.08 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3598.951, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.94 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.89 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3600.951, Time=0.21 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.051 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1795.475
Date:                Sun, 12 Dec 2021   AIC                           3598.951
Time:                        15:40:07   BIC                           3617.714
Sample:                             0   HQIC                          3606.157
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1983      0.003   -389.581      0.000      -1.204      -1.192
ar.L2         -0.8973      0.006   -139.732      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.649      0.000      -0.410      -0.387
sigma2         5.0573      0.023    215.292      0.000       5.011       5.103
===================================================================================
Ljung-Box (L1) (Q):                  14.41   Jarque-Bera (JB):           2460553.80
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.89
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.74
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_51 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_51 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.41242, saving model to LSTM1.h5
10/10 - 2s - loss: 0.3561 - val_loss: 0.4124 - lr: 0.0010 - 2s/epoch - 183ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.41242 to 0.05022, saving model to LSTM1.h5
10/10 - 0s - loss: 0.3033 - val_loss: 0.0502 - lr: 0.0010 - 121ms/epoch - 12ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05022
10/10 - 0s - loss: 0.1002 - val_loss: 0.2789 - lr: 0.0010 - 100ms/epoch - 10ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05022
10/10 - 0s - loss: 0.0701 - val_loss: 0.3588 - lr: 0.0010 - 110ms/epoch - 11ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05022
10/10 - 0s - loss: 0.0564 - val_loss: 0.1735 - lr: 0.0010 - 102ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.05022
10/10 - 0s - loss: 0.0572 - val_loss: 0.0565 - lr: 0.0010 - 113ms/epoch - 11ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.05022 to 0.02965, saving model to LSTM1.h5
10/10 - 0s - loss: 0.0522 - val_loss: 0.0296 - lr: 0.0010 - 142ms/epoch - 14ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0440 - val_loss: 0.1053 - lr: 0.0010 - 115ms/epoch - 12ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0428 - val_loss: 0.1048 - lr: 0.0010 - 115ms/epoch - 11ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0406 - val_loss: 0.0567 - lr: 0.0010 - 104ms/epoch - 10ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0399 - val_loss: 0.0881 - lr: 0.0010 - 100ms/epoch - 10ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00012: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0338 - val_loss: 0.0729 - lr: 0.0010 - 132ms/epoch - 13ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0333 - val_loss: 0.0715 - lr: 1.0000e-04 - 107ms/epoch - 11ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0354 - val_loss: 0.0703 - lr: 1.0000e-04 - 127ms/epoch - 13ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0331 - val_loss: 0.0707 - lr: 1.0000e-04 - 114ms/epoch - 11ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0321 - val_loss: 0.0720 - lr: 1.0000e-04 - 92ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00017: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0338 - val_loss: 0.0712 - lr: 1.0000e-04 - 107ms/epoch - 11ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0312 - val_loss: 0.0709 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0333 - val_loss: 0.0712 - lr: 1.0000e-05 - 101ms/epoch - 10ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0327 - val_loss: 0.0712 - lr: 1.0000e-05 - 101ms/epoch - 10ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0347 - val_loss: 0.0712 - lr: 1.0000e-05 - 105ms/epoch - 11ms/step
Epoch 22/500

Epoch 00022: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00022: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0346 - val_loss: 0.0711 - lr: 1.0000e-05 - 108ms/epoch - 11ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0321 - val_loss: 0.0714 - lr: 1.0000e-05 - 93ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0330 - val_loss: 0.0714 - lr: 1.0000e-05 - 125ms/epoch - 12ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0308 - val_loss: 0.0715 - lr: 1.0000e-05 - 114ms/epoch - 11ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0346 - val_loss: 0.0716 - lr: 1.0000e-05 - 105ms/epoch - 10ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0335 - val_loss: 0.0716 - lr: 1.0000e-05 - 121ms/epoch - 12ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0313 - val_loss: 0.0712 - lr: 1.0000e-05 - 114ms/epoch - 11ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0339 - val_loss: 0.0710 - lr: 1.0000e-05 - 93ms/epoch - 9ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0324 - val_loss: 0.0709 - lr: 1.0000e-05 - 132ms/epoch - 13ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0294 - val_loss: 0.0711 - lr: 1.0000e-05 - 103ms/epoch - 10ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0340 - val_loss: 0.0712 - lr: 1.0000e-05 - 133ms/epoch - 13ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0329 - val_loss: 0.0714 - lr: 1.0000e-05 - 124ms/epoch - 12ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0329 - val_loss: 0.0713 - lr: 1.0000e-05 - 104ms/epoch - 10ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0325 - val_loss: 0.0713 - lr: 1.0000e-05 - 96ms/epoch - 10ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0329 - val_loss: 0.0714 - lr: 1.0000e-05 - 118ms/epoch - 12ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0343 - val_loss: 0.0712 - lr: 1.0000e-05 - 103ms/epoch - 10ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0338 - val_loss: 0.0714 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0333 - val_loss: 0.0716 - lr: 1.0000e-05 - 107ms/epoch - 11ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0340 - val_loss: 0.0716 - lr: 1.0000e-05 - 119ms/epoch - 12ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0354 - val_loss: 0.0713 - lr: 1.0000e-05 - 134ms/epoch - 13ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0319 - val_loss: 0.0711 - lr: 1.0000e-05 - 110ms/epoch - 11ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0341 - val_loss: 0.0709 - lr: 1.0000e-05 - 108ms/epoch - 11ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0320 - val_loss: 0.0706 - lr: 1.0000e-05 - 103ms/epoch - 10ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0311 - val_loss: 0.0700 - lr: 1.0000e-05 - 104ms/epoch - 10ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0299 - val_loss: 0.0702 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0322 - val_loss: 0.0701 - lr: 1.0000e-05 - 122ms/epoch - 12ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0323 - val_loss: 0.0701 - lr: 1.0000e-05 - 114ms/epoch - 11ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0317 - val_loss: 0.0701 - lr: 1.0000e-05 - 137ms/epoch - 14ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0329 - val_loss: 0.0704 - lr: 1.0000e-05 - 113ms/epoch - 11ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0340 - val_loss: 0.0705 - lr: 1.0000e-05 - 109ms/epoch - 11ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0309 - val_loss: 0.0704 - lr: 1.0000e-05 - 123ms/epoch - 12ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0292 - val_loss: 0.0704 - lr: 1.0000e-05 - 99ms/epoch - 10ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0322 - val_loss: 0.0699 - lr: 1.0000e-05 - 92ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0323 - val_loss: 0.0698 - lr: 1.0000e-05 - 97ms/epoch - 10ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0343 - val_loss: 0.0697 - lr: 1.0000e-05 - 110ms/epoch - 11ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.02965
10/10 - 0s - loss: 0.0348 - val_loss: 0.0696 - lr: 1.0000e-05 - 102ms/epoch - 10ms/step
Epoch 00057: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965

WMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 80.72598349686672 
RMSE:	 8.9847639644493 
MAPE:	 7.266216353433966

DEMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 74.8946292448382 
RMSE:	 8.654168316183721 
MAPE:	 7.175854729849037
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.35 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4190.464, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3724.371, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.27 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3494.154, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3357.435, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.22 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.72 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3359.435, Time=0.20 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.000 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1674.717
Date:                Sun, 12 Dec 2021   AIC                           3357.435
Time:                        15:41:18   BIC                           3376.198
Sample:                             0   HQIC                          3364.641
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1955      0.003   -381.246      0.000      -1.202      -1.189
ar.L2         -0.8964      0.007   -135.835      0.000      -0.909      -0.883
ar.L3         -0.3971      0.006    -67.229      0.000      -0.409      -0.385
sigma2         3.7466      0.018    211.623      0.000       3.712       3.781
===================================================================================
Ljung-Box (L1) (Q):                  14.20   Jarque-Bera (JB):           2338363.32
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             3.76
Prob(H) (two-sided):                  0.00   Kurtosis:                       266.93
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_52 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_52 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05005, saving model to LSTM1.h5
45/45 - 2s - loss: 0.2498 - val_loss: 0.0501 - lr: 0.0010 - 2s/epoch - 39ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0990 - val_loss: 0.1124 - lr: 0.0010 - 421ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05005
45/45 - 1s - loss: 0.0618 - val_loss: 0.7416 - lr: 0.0010 - 527ms/epoch - 12ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0480 - val_loss: 0.3445 - lr: 0.0010 - 390ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0459 - val_loss: 0.0789 - lr: 0.0010 - 392ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0464 - val_loss: 0.3272 - lr: 0.0010 - 390ms/epoch - 9ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0477 - val_loss: 0.2992 - lr: 1.0000e-04 - 377ms/epoch - 8ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0422 - val_loss: 0.2710 - lr: 1.0000e-04 - 401ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0349 - val_loss: 0.2479 - lr: 1.0000e-04 - 368ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0349 - val_loss: 0.2289 - lr: 1.0000e-04 - 404ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0361 - val_loss: 0.2117 - lr: 1.0000e-04 - 403ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0343 - val_loss: 0.2096 - lr: 1.0000e-05 - 356ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0356 - val_loss: 0.2075 - lr: 1.0000e-05 - 361ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0331 - val_loss: 0.2053 - lr: 1.0000e-05 - 449ms/epoch - 10ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0319 - val_loss: 0.2034 - lr: 1.0000e-05 - 367ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0346 - val_loss: 0.2010 - lr: 1.0000e-05 - 368ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0329 - val_loss: 0.1987 - lr: 1.0000e-05 - 413ms/epoch - 9ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0316 - val_loss: 0.1968 - lr: 1.0000e-05 - 387ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0346 - val_loss: 0.1946 - lr: 1.0000e-05 - 424ms/epoch - 9ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0320 - val_loss: 0.1928 - lr: 1.0000e-05 - 411ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0318 - val_loss: 0.1909 - lr: 1.0000e-05 - 410ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0322 - val_loss: 0.1890 - lr: 1.0000e-05 - 453ms/epoch - 10ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0350 - val_loss: 0.1881 - lr: 1.0000e-05 - 359ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0351 - val_loss: 0.1859 - lr: 1.0000e-05 - 399ms/epoch - 9ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0314 - val_loss: 0.1844 - lr: 1.0000e-05 - 391ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0323 - val_loss: 0.1824 - lr: 1.0000e-05 - 391ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0350 - val_loss: 0.1806 - lr: 1.0000e-05 - 414ms/epoch - 9ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0326 - val_loss: 0.1782 - lr: 1.0000e-05 - 406ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0315 - val_loss: 0.1759 - lr: 1.0000e-05 - 368ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0316 - val_loss: 0.1735 - lr: 1.0000e-05 - 426ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0329 - val_loss: 0.1711 - lr: 1.0000e-05 - 376ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0317 - val_loss: 0.1687 - lr: 1.0000e-05 - 419ms/epoch - 9ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0305 - val_loss: 0.1668 - lr: 1.0000e-05 - 406ms/epoch - 9ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0326 - val_loss: 0.1648 - lr: 1.0000e-05 - 429ms/epoch - 10ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0335 - val_loss: 0.1631 - lr: 1.0000e-05 - 375ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0356 - val_loss: 0.1611 - lr: 1.0000e-05 - 384ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0321 - val_loss: 0.1597 - lr: 1.0000e-05 - 398ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0300 - val_loss: 0.1575 - lr: 1.0000e-05 - 467ms/epoch - 10ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0322 - val_loss: 0.1557 - lr: 1.0000e-05 - 437ms/epoch - 10ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0322 - val_loss: 0.1546 - lr: 1.0000e-05 - 413ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0317 - val_loss: 0.1525 - lr: 1.0000e-05 - 457ms/epoch - 10ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0308 - val_loss: 0.1512 - lr: 1.0000e-05 - 391ms/epoch - 9ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0309 - val_loss: 0.1494 - lr: 1.0000e-05 - 404ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0304 - val_loss: 0.1479 - lr: 1.0000e-05 - 409ms/epoch - 9ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0319 - val_loss: 0.1458 - lr: 1.0000e-05 - 405ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0291 - val_loss: 0.1441 - lr: 1.0000e-05 - 425ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0314 - val_loss: 0.1428 - lr: 1.0000e-05 - 413ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0306 - val_loss: 0.1408 - lr: 1.0000e-05 - 391ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0330 - val_loss: 0.1393 - lr: 1.0000e-05 - 397ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0319 - val_loss: 0.1371 - lr: 1.0000e-05 - 380ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05005
45/45 - 0s - loss: 0.0272 - val_loss: 0.1343 - lr: 1.0000e-05 - 375ms/epoch - 8ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965

WMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 80.72598349686672 
RMSE:	 8.9847639644493 
MAPE:	 7.266216353433966

DEMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 74.8946292448382 
RMSE:	 8.654168316183721 
MAPE:	 7.175854729849037

KAMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 23.77011386693893 
RMSE:	 4.87546037487117 
MAPE:	 3.900500517739451
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4212.289, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3747.746, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.23 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3523.401, Time=0.08 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3387.759, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.26 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.86 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3389.758, Time=0.22 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.186 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1689.879
Date:                Sun, 12 Dec 2021   AIC                           3387.759
Time:                        15:42:48   BIC                           3406.522
Sample:                             0   HQIC                          3394.964
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1878      0.003   -345.315      0.000      -1.195      -1.181
ar.L2         -0.8876      0.007   -121.809      0.000      -0.902      -0.873
ar.L3         -0.3957      0.007    -60.127      0.000      -0.409      -0.383
sigma2         3.8904      0.020    193.404      0.000       3.851       3.930
===================================================================================
Ljung-Box (L1) (Q):                  13.21   Jarque-Bera (JB):           1659080.01
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.08   Skew:                             3.28
Prob(H) (two-sided):                  0.00   Kurtosis:                       225.31
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_53 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_53 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.14279, saving model to LSTM1.h5
58/58 - 2s - loss: 0.1302 - val_loss: 0.1428 - lr: 0.0010 - 2s/epoch - 32ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.14279 to 0.01434, saving model to LSTM1.h5
58/58 - 1s - loss: 0.0798 - val_loss: 0.0143 - lr: 0.0010 - 526ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0806 - val_loss: 0.4012 - lr: 0.0010 - 519ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0506 - val_loss: 0.1824 - lr: 0.0010 - 496ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0458 - val_loss: 0.2783 - lr: 0.0010 - 514ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0477 - val_loss: 0.1316 - lr: 0.0010 - 489ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0366 - val_loss: 0.4080 - lr: 0.0010 - 498ms/epoch - 9ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0405 - val_loss: 0.3991 - lr: 1.0000e-04 - 500ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0389 - val_loss: 0.3873 - lr: 1.0000e-04 - 541ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0327 - val_loss: 0.3737 - lr: 1.0000e-04 - 505ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0310 - val_loss: 0.3594 - lr: 1.0000e-04 - 491ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00012: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0353 - val_loss: 0.3431 - lr: 1.0000e-04 - 474ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0296 - val_loss: 0.3414 - lr: 1.0000e-05 - 505ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0318 - val_loss: 0.3398 - lr: 1.0000e-05 - 483ms/epoch - 8ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0292 - val_loss: 0.3380 - lr: 1.0000e-05 - 514ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0300 - val_loss: 0.3362 - lr: 1.0000e-05 - 527ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00017: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0284 - val_loss: 0.3343 - lr: 1.0000e-05 - 559ms/epoch - 10ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0284 - val_loss: 0.3324 - lr: 1.0000e-05 - 521ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0289 - val_loss: 0.3305 - lr: 1.0000e-05 - 486ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0283 - val_loss: 0.3286 - lr: 1.0000e-05 - 525ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0308 - val_loss: 0.3270 - lr: 1.0000e-05 - 487ms/epoch - 8ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0271 - val_loss: 0.3252 - lr: 1.0000e-05 - 491ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0304 - val_loss: 0.3233 - lr: 1.0000e-05 - 457ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0297 - val_loss: 0.3213 - lr: 1.0000e-05 - 465ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0288 - val_loss: 0.3194 - lr: 1.0000e-05 - 536ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0296 - val_loss: 0.3175 - lr: 1.0000e-05 - 521ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0299 - val_loss: 0.3156 - lr: 1.0000e-05 - 471ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0288 - val_loss: 0.3136 - lr: 1.0000e-05 - 469ms/epoch - 8ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0326 - val_loss: 0.3117 - lr: 1.0000e-05 - 470ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0294 - val_loss: 0.3094 - lr: 1.0000e-05 - 513ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0315 - val_loss: 0.3075 - lr: 1.0000e-05 - 461ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0284 - val_loss: 0.3050 - lr: 1.0000e-05 - 491ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0293 - val_loss: 0.3026 - lr: 1.0000e-05 - 532ms/epoch - 9ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0277 - val_loss: 0.3006 - lr: 1.0000e-05 - 474ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0311 - val_loss: 0.2982 - lr: 1.0000e-05 - 484ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0263 - val_loss: 0.2960 - lr: 1.0000e-05 - 457ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0280 - val_loss: 0.2939 - lr: 1.0000e-05 - 506ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0268 - val_loss: 0.2915 - lr: 1.0000e-05 - 501ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0278 - val_loss: 0.2893 - lr: 1.0000e-05 - 509ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0280 - val_loss: 0.2876 - lr: 1.0000e-05 - 507ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0299 - val_loss: 0.2856 - lr: 1.0000e-05 - 569ms/epoch - 10ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0282 - val_loss: 0.2833 - lr: 1.0000e-05 - 470ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0287 - val_loss: 0.2808 - lr: 1.0000e-05 - 478ms/epoch - 8ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0275 - val_loss: 0.2786 - lr: 1.0000e-05 - 464ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0273 - val_loss: 0.2764 - lr: 1.0000e-05 - 500ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0299 - val_loss: 0.2741 - lr: 1.0000e-05 - 458ms/epoch - 8ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0291 - val_loss: 0.2718 - lr: 1.0000e-05 - 500ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0270 - val_loss: 0.2689 - lr: 1.0000e-05 - 477ms/epoch - 8ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0272 - val_loss: 0.2661 - lr: 1.0000e-05 - 476ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.01434
58/58 - 0s - loss: 0.0293 - val_loss: 0.2642 - lr: 1.0000e-05 - 491ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0285 - val_loss: 0.2622 - lr: 1.0000e-05 - 536ms/epoch - 9ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01434
58/58 - 1s - loss: 0.0268 - val_loss: 0.2603 - lr: 1.0000e-05 - 540ms/epoch - 9ms/step
Epoch 00052: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965

WMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 80.72598349686672 
RMSE:	 8.9847639644493 
MAPE:	 7.266216353433966

DEMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 74.8946292448382 
RMSE:	 8.654168316183721 
MAPE:	 7.175854729849037

KAMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 23.77011386693893 
RMSE:	 4.87546037487117 
MAPE:	 3.900500517739451

MIDPOINT
Prediction vs Close:		48.88% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 53.557107319764015 
RMSE:	 7.318272153983071 
MAPE:	 6.3365268769325365
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.38 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4414.515, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3944.062, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.35 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3715.173, Time=0.06 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3577.471, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.43 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.57 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3579.471, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.150 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1784.736
Date:                Sun, 12 Dec 2021   AIC                           3577.471
Time:                        15:44:39   BIC                           3596.235
Sample:                             0   HQIC                          3584.677
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.844      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.861      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.862      0.000      -0.410      -0.387
sigma2         4.9242      0.023    215.469      0.000       4.879       4.969
===================================================================================
Ljung-Box (L1) (Q):                  14.55   Jarque-Bera (JB):           2468024.38
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       274.15
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_54 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_54 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.38569, saving model to LSTM1.h5
43/43 - 2s - loss: 0.3356 - val_loss: 0.3857 - lr: 0.0010 - 2s/epoch - 50ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.38569 to 0.08604, saving model to LSTM1.h5
43/43 - 0s - loss: 0.1240 - val_loss: 0.0860 - lr: 0.0010 - 374ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0693 - val_loss: 0.4426 - lr: 0.0010 - 350ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0472 - val_loss: 0.1786 - lr: 0.0010 - 374ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0524 - val_loss: 0.1600 - lr: 0.0010 - 366ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0428 - val_loss: 0.1204 - lr: 0.0010 - 356ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0420 - val_loss: 0.1158 - lr: 0.0010 - 432ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0410 - val_loss: 0.1228 - lr: 1.0000e-04 - 388ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0408 - val_loss: 0.1227 - lr: 1.0000e-04 - 393ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0361 - val_loss: 0.1239 - lr: 1.0000e-04 - 362ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0363 - val_loss: 0.1258 - lr: 1.0000e-04 - 347ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00012: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0383 - val_loss: 0.1278 - lr: 1.0000e-04 - 449ms/epoch - 10ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0341 - val_loss: 0.1277 - lr: 1.0000e-05 - 393ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0332 - val_loss: 0.1273 - lr: 1.0000e-05 - 407ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0342 - val_loss: 0.1272 - lr: 1.0000e-05 - 367ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0372 - val_loss: 0.1268 - lr: 1.0000e-05 - 387ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00017: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0350 - val_loss: 0.1272 - lr: 1.0000e-05 - 375ms/epoch - 9ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0319 - val_loss: 0.1271 - lr: 1.0000e-05 - 392ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0359 - val_loss: 0.1269 - lr: 1.0000e-05 - 354ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0331 - val_loss: 0.1269 - lr: 1.0000e-05 - 420ms/epoch - 10ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0327 - val_loss: 0.1265 - lr: 1.0000e-05 - 383ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0321 - val_loss: 0.1261 - lr: 1.0000e-05 - 424ms/epoch - 10ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0315 - val_loss: 0.1260 - lr: 1.0000e-05 - 377ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0332 - val_loss: 0.1252 - lr: 1.0000e-05 - 363ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0325 - val_loss: 0.1243 - lr: 1.0000e-05 - 347ms/epoch - 8ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0336 - val_loss: 0.1244 - lr: 1.0000e-05 - 377ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0329 - val_loss: 0.1248 - lr: 1.0000e-05 - 350ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0349 - val_loss: 0.1242 - lr: 1.0000e-05 - 368ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0342 - val_loss: 0.1239 - lr: 1.0000e-05 - 368ms/epoch - 9ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0329 - val_loss: 0.1240 - lr: 1.0000e-05 - 355ms/epoch - 8ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0342 - val_loss: 0.1237 - lr: 1.0000e-05 - 368ms/epoch - 9ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0331 - val_loss: 0.1236 - lr: 1.0000e-05 - 341ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0362 - val_loss: 0.1229 - lr: 1.0000e-05 - 363ms/epoch - 8ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0321 - val_loss: 0.1221 - lr: 1.0000e-05 - 354ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0341 - val_loss: 0.1219 - lr: 1.0000e-05 - 398ms/epoch - 9ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0336 - val_loss: 0.1221 - lr: 1.0000e-05 - 338ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0313 - val_loss: 0.1233 - lr: 1.0000e-05 - 394ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0305 - val_loss: 0.1227 - lr: 1.0000e-05 - 407ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0334 - val_loss: 0.1218 - lr: 1.0000e-05 - 362ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0305 - val_loss: 0.1214 - lr: 1.0000e-05 - 361ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0316 - val_loss: 0.1210 - lr: 1.0000e-05 - 361ms/epoch - 8ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0332 - val_loss: 0.1213 - lr: 1.0000e-05 - 458ms/epoch - 11ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0319 - val_loss: 0.1218 - lr: 1.0000e-05 - 374ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0301 - val_loss: 0.1210 - lr: 1.0000e-05 - 380ms/epoch - 9ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0321 - val_loss: 0.1206 - lr: 1.0000e-05 - 391ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0331 - val_loss: 0.1220 - lr: 1.0000e-05 - 390ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0297 - val_loss: 0.1226 - lr: 1.0000e-05 - 389ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0310 - val_loss: 0.1226 - lr: 1.0000e-05 - 368ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0328 - val_loss: 0.1227 - lr: 1.0000e-05 - 401ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0344 - val_loss: 0.1212 - lr: 1.0000e-05 - 392ms/epoch - 9ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0306 - val_loss: 0.1207 - lr: 1.0000e-05 - 357ms/epoch - 8ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.08604
43/43 - 0s - loss: 0.0334 - val_loss: 0.1188 - lr: 1.0000e-05 - 387ms/epoch - 9ms/step
Epoch 00052: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965

WMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 80.72598349686672 
RMSE:	 8.9847639644493 
MAPE:	 7.266216353433966

DEMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 74.8946292448382 
RMSE:	 8.654168316183721 
MAPE:	 7.175854729849037

KAMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 23.77011386693893 
RMSE:	 4.87546037487117 
MAPE:	 3.900500517739451

MIDPOINT
Prediction vs Close:		48.88% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 53.557107319764015 
RMSE:	 7.318272153983071 
MAPE:	 6.3365268769325365

T3
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 44.09609147416104 
RMSE:	 6.640488797834165 
MAPE:	 5.406095596816415
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.46 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4352.703, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3889.412, Time=0.06 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.26 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3689.930, Time=0.06 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3574.245, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.10 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.76 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3576.245, Time=0.18 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.007 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1783.123
Date:                Sun, 12 Dec 2021   AIC                           3574.245
Time:                        15:46:04   BIC                           3593.008
Sample:                             0   HQIC                          3581.451
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1480      0.004   -302.430      0.000      -1.155      -1.141
ar.L2         -0.8300      0.008    -99.682      0.000      -0.846      -0.814
ar.L3         -0.3687      0.007    -50.527      0.000      -0.383      -0.354
sigma2         4.9055      0.028    175.970      0.000       4.851       4.960
===================================================================================
Ljung-Box (L1) (Q):                  11.61   Jarque-Bera (JB):           1261976.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.16   Skew:                             2.52
Prob(H) (two-sided):                  0.00   Kurtosis:                       196.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

WARNING:tensorflow:Layer lstm_55 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_55 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.09097, saving model to LSTM1.h5
90/90 - 2s - loss: 0.1656 - val_loss: 0.0910 - lr: 0.0010 - 2s/epoch - 23ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0915 - val_loss: 0.5898 - lr: 0.0010 - 711ms/epoch - 8ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0636 - val_loss: 0.1137 - lr: 0.0010 - 679ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0600 - val_loss: 0.1295 - lr: 0.0010 - 689ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0556 - val_loss: 0.6111 - lr: 0.0010 - 743ms/epoch - 8ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0589 - val_loss: 0.4250 - lr: 0.0010 - 706ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0551 - val_loss: 0.4149 - lr: 1.0000e-04 - 690ms/epoch - 8ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0426 - val_loss: 0.3938 - lr: 1.0000e-04 - 716ms/epoch - 8ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0401 - val_loss: 0.3687 - lr: 1.0000e-04 - 690ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0411 - val_loss: 0.3431 - lr: 1.0000e-04 - 692ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0357 - val_loss: 0.3219 - lr: 1.0000e-04 - 687ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0310 - val_loss: 0.3199 - lr: 1.0000e-05 - 723ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0342 - val_loss: 0.3176 - lr: 1.0000e-05 - 719ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0356 - val_loss: 0.3154 - lr: 1.0000e-05 - 771ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0364 - val_loss: 0.3131 - lr: 1.0000e-05 - 715ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0366 - val_loss: 0.3111 - lr: 1.0000e-05 - 748ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0359 - val_loss: 0.3093 - lr: 1.0000e-05 - 701ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0313 - val_loss: 0.3064 - lr: 1.0000e-05 - 756ms/epoch - 8ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0336 - val_loss: 0.3035 - lr: 1.0000e-05 - 674ms/epoch - 7ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0368 - val_loss: 0.3007 - lr: 1.0000e-05 - 671ms/epoch - 7ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0358 - val_loss: 0.2980 - lr: 1.0000e-05 - 690ms/epoch - 8ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0337 - val_loss: 0.2950 - lr: 1.0000e-05 - 692ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0341 - val_loss: 0.2921 - lr: 1.0000e-05 - 691ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0359 - val_loss: 0.2898 - lr: 1.0000e-05 - 682ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0334 - val_loss: 0.2868 - lr: 1.0000e-05 - 726ms/epoch - 8ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0332 - val_loss: 0.2837 - lr: 1.0000e-05 - 719ms/epoch - 8ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0312 - val_loss: 0.2804 - lr: 1.0000e-05 - 720ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0331 - val_loss: 0.2768 - lr: 1.0000e-05 - 703ms/epoch - 8ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0308 - val_loss: 0.2736 - lr: 1.0000e-05 - 715ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0301 - val_loss: 0.2710 - lr: 1.0000e-05 - 777ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0317 - val_loss: 0.2682 - lr: 1.0000e-05 - 681ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0358 - val_loss: 0.2650 - lr: 1.0000e-05 - 721ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0313 - val_loss: 0.2624 - lr: 1.0000e-05 - 729ms/epoch - 8ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0297 - val_loss: 0.2587 - lr: 1.0000e-05 - 681ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0312 - val_loss: 0.2557 - lr: 1.0000e-05 - 704ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0315 - val_loss: 0.2533 - lr: 1.0000e-05 - 700ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0300 - val_loss: 0.2504 - lr: 1.0000e-05 - 710ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0309 - val_loss: 0.2474 - lr: 1.0000e-05 - 702ms/epoch - 8ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0303 - val_loss: 0.2447 - lr: 1.0000e-05 - 694ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0328 - val_loss: 0.2420 - lr: 1.0000e-05 - 754ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0285 - val_loss: 0.2388 - lr: 1.0000e-05 - 700ms/epoch - 8ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0309 - val_loss: 0.2357 - lr: 1.0000e-05 - 697ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0290 - val_loss: 0.2329 - lr: 1.0000e-05 - 672ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0290 - val_loss: 0.2306 - lr: 1.0000e-05 - 770ms/epoch - 9ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0279 - val_loss: 0.2281 - lr: 1.0000e-05 - 743ms/epoch - 8ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0310 - val_loss: 0.2255 - lr: 1.0000e-05 - 707ms/epoch - 8ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0298 - val_loss: 0.2221 - lr: 1.0000e-05 - 685ms/epoch - 8ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0299 - val_loss: 0.2197 - lr: 1.0000e-05 - 689ms/epoch - 8ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0311 - val_loss: 0.2173 - lr: 1.0000e-05 - 705ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0267 - val_loss: 0.2149 - lr: 1.0000e-05 - 759ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.09097
90/90 - 1s - loss: 0.0290 - val_loss: 0.2122 - lr: 1.0000e-05 - 700ms/epoch - 8ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 29.509403666146007 
RMSE:	 5.432255854260365 
MAPE:	 4.5288133477558885

EMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	50.37% Accuracy
MSE:	 28.603272766421263 
RMSE:	 5.348202760406646 
MAPE:	 4.3952252144553965

WMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 80.72598349686672 
RMSE:	 8.9847639644493 
MAPE:	 7.266216353433966

DEMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 74.8946292448382 
RMSE:	 8.654168316183721 
MAPE:	 7.175854729849037

KAMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 23.77011386693893 
RMSE:	 4.87546037487117 
MAPE:	 3.900500517739451

MIDPOINT
Prediction vs Close:		48.88% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 53.557107319764015 
RMSE:	 7.318272153983071 
MAPE:	 6.3365268769325365

T3
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 44.09609147416104 
RMSE:	 6.640488797834165 
MAPE:	 5.406095596816415

TEMA
Prediction vs Close:		45.52% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 9.564405293897392 
RMSE:	 3.0926372716336124 
MAPE:	 2.44888799215368
Runtime: mins: 12.028950116466664

Architecture used

In [104]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
In [105]:
img = cv2.imread('Experiment1.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[105]:
<matplotlib.image.AxesImage at 0x7fa5ea520ad0>

Excess kurtosis is a metric that compares the kurtosis of a distribution against the kurtosis of a normal distribution. The kurtosis of a normal distribution equals 3. Therefore, the excess kurtosis is found using the formula below:

Excess Kurtosis = Kurtosis – 3

Model Plots

In [106]:
np.save("X_train_appl.npy", X_train)
np.save("y_train_appl.npy", y_train)
np.save("X_test_appl.npy", X_test)
np.save("y_test_appl.npy", y_test)
np.save("yc_train_appl.npy", yc_train)
np.save("yc_test_appl.npy", yc_test)
np.save('index_train_appl.npy', index_train)
np.save('index_test_appl.npy', index_test)
In [107]:
list(simulation1.keys())
Out[107]:
['SMA', 'EMA', 'WMA', 'DEMA', 'KAMA', 'MIDPOINT', 'T3', 'TEMA']
In [108]:
for i in range(len(list(simulation1.keys()))):
  SIM = list(simulation1.keys())[i]
  plot_train(simulation1,SIM)
  plot_test(simulation1,SIM)
----- Train RMSE for SMA ----- 7.895573621826288
----- Train_MSE_LSTM for SMA ----- 62.34008281767908
----- Train MAE LSTM for SMA ----- 6.764936516294256
----- Test RMSE for SMA----- 5.432255854260365
----- Test_MSE_LSTM for SMA----- 29.509403666146007
----- Test_MAE_LSTM for SMA----- 4.5288133477558885
----- Train RMSE for EMA ----- 8.87769382903084
----- Train_MSE_LSTM for EMA ----- 78.81344772201226
----- Train MAE LSTM for EMA ----- 7.790667497096491
----- Test RMSE for EMA----- 5.348202760406646
----- Test_MSE_LSTM for EMA----- 28.603272766421263
----- Test_MAE_LSTM for EMA----- 4.3952252144553965
----- Train RMSE for WMA ----- 9.703993025462333
----- Train_MSE_LSTM for WMA ----- 94.16748063822159
----- Train MAE LSTM for WMA ----- 8.628062737580057
----- Test RMSE for WMA----- 8.9847639644493
----- Test_MSE_LSTM for WMA----- 80.72598349686672
----- Test_MAE_LSTM for WMA----- 7.266216353433966
----- Train RMSE for DEMA ----- 11.240117396029953
----- Train_MSE_LSTM for DEMA ----- 126.3402390765352
----- Train MAE LSTM for DEMA ----- 10.016611697382782
----- Test RMSE for DEMA----- 8.654168316183721
----- Test_MSE_LSTM for DEMA----- 74.8946292448382
----- Test_MAE_LSTM for DEMA----- 7.175854729849037
----- Train RMSE for KAMA ----- 9.358621140016876
----- Train_MSE_LSTM for KAMA ----- 87.58378964237077
----- Train MAE LSTM for KAMA ----- 8.463473867850913
----- Test RMSE for KAMA----- 4.87546037487117
----- Test_MSE_LSTM for KAMA----- 23.77011386693893
----- Test_MAE_LSTM for KAMA----- 3.900500517739451
----- Train RMSE for MIDPOINT ----- 8.349312336425948
----- Train_MSE_LSTM for MIDPOINT ----- 69.71101649119451
----- Train MAE LSTM for MIDPOINT ----- 7.472949316300968
----- Test RMSE for MIDPOINT----- 7.318272153983071
----- Test_MSE_LSTM for MIDPOINT----- 53.557107319764015
----- Test_MAE_LSTM for MIDPOINT----- 6.3365268769325365
----- Train RMSE for T3 ----- 10.91415603392602
----- Train_MSE_LSTM for T3 ----- 119.11880193288376
----- Train MAE LSTM for T3 ----- 9.801440406699038
----- Test RMSE for T3----- 6.640488797834165
----- Test_MSE_LSTM for T3----- 44.09609147416104
----- Test_MAE_LSTM for T3----- 5.406095596816415
----- Train RMSE for TEMA ----- 6.523327083998898
----- Train_MSE_LSTM for TEMA ----- 42.55379624483356
----- Train MAE LSTM for TEMA ----- 4.644936803645353
----- Test RMSE for TEMA----- 3.0926372716336124
----- Test_MSE_LSTM for TEMA----- 9.564405293897392
----- Test_MAE_LSTM for TEMA----- 2.44888799215368

Univariate Arima Multistep MutiVariate LSTM Hybrid Model Experiment 2

In [87]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det = 20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # # Option 1
    # # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    # model.add(Dense(units=64,activation='relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    # ## Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()


    # option 2
    model = Sequential()
    model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    model.add(Dense(64))
    model.add(Dense(units=output_dim))
    model.compile(optimizer=Adam(learning_rate = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM2.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()




    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    # class Double_Tanh(Activation):
    #     def __init__(self, activation, **kwargs):
    #         super(Double_Tanh, self).__init__(activation, **kwargs)
    #         self.__name__ = 'double_tanh'

    # def double_tanh(x):
    #     return (K.tanh(x) * 2)

    # get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
    #     # Model Generation
    # model = Sequential()
    # #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    # model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    # model.add(Dense(1))
    # model.add(Activation(double_tanh))
    # model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(x_train.shape[1], 1)))
    # model.add(LSTM(units=int(lstm_len/2)))
    # model.add(Dense(1, activation='sigmoid'))
    # model.compile(loss='mean_squared_error', optimizer='adam')
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [88]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation2 = {}
    imgfile = 'Experiment2'
    for ma in optimized_period:
              print(ma)
              print(functions[ma])
              print ( int( optimized_period[ma]))
            # if ma == 'SMA':
              low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
              low_vol = low_vol.fillna(0)
              low_vol_data = df['close']
              high_vol = pd.DataFrame()
              df2 = df.copy()
              for i in df2.columns:
                if i in low_vol.columns:
                  high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
              high_vol_data = df['close']
              ## *****************************************************
              # Generate ARIMA and LSTM predictions
              print('\nWorking on ' + ma + ' predictions')
              try:
                print('parameters used : ', train_len, test_len)
                low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima(low_vol,low_vol_data, train_len, test_len)
              except:
                  print('ARIMA error, skipping to next MA type')
                  continue
              Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
              final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
              mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
              rmse_ftr = mse_ftr ** 0.5
              mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
              mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

              final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
              mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
              rmse = mse ** 0.5
              mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              # Generate prediction accuracy
              actual = df['close'].tail(test_len).values
              result_1 = []
              result_2 = []
              for i in range(1, len(final_prediction)):
                  # Compare prediction to previous close price
                  if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                      result_1.append(1)
                  elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                      result_1.append(1)
                  else:
                      result_1.append(0)

                  # Compare prediction to previous prediction
                  if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                      result_2.append(1)
                  elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                      result_2.append(1)
                  else:
                      result_2.append(0)

              accuracy_1 = np.mean(result_1)
              accuracy_2 = np.mean(result_2)

              simulation2[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                            'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                            'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                            'rmse': rmse_ftr, 'mae' : mae_ftr},
                                'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                          'rmse': rmse, 'mae': mae },
                                'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

              # save simulation data here as checkpoint
              with open('simulation2_data.json', 'w') as fp:
                  json.dump(simulation2, fp)

              for ma in simulation2.keys():
                  print('\n' + ma)
                  print('Prediction vs Close:\t\t' + str(round(100*simulation2[ma]['accuracy']['prediction vs close'], 2))
                        + '% Accuracy')
                  print('Prediction vs Prediction:\t' + str(round(100*simulation2[ma]['accuracy']['prediction vs prediction'], 2))
                        + '% Accuracy')
                  print('MSE:\t', simulation2[ma]['final']['mse'],
                        '\nRMSE:\t', simulation2[ma]['final']['rmse'],
                        '\nMAPE:\t', simulation2[ma]['final']['mae'])#,
                        # '\nMAPE:\t', simulation[ma]['final']['mape'])
            # else:
            #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.49 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4157.020, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3687.148, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.20 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3458.651, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3322.133, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.78 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.78 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3324.133, Time=0.23 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.721 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1657.067
Date:                Sun, 12 Dec 2021   AIC                           3322.133
Time:                        14:39:19   BIC                           3340.897
Sample:                             0   HQIC                          3329.339
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1966      0.003   -387.226      0.000      -1.203      -1.191
ar.L2         -0.8952      0.006   -138.692      0.000      -0.908      -0.883
ar.L3         -0.3968      0.006    -68.284      0.000      -0.408      -0.385
sigma2         3.5858      0.017    214.535      0.000       3.553       3.619
===================================================================================
Ljung-Box (L1) (Q):                  14.47   Jarque-Bera (JB):           2428881.42
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       271.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.07969, saving model to LSTM2.h5
48/48 - 5s - loss: 0.1695 - accuracy: 0.0000e+00 - val_loss: 0.0797 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 5s/epoch - 107ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.07969 to 0.01348, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0739 - accuracy: 0.0000e+00 - val_loss: 0.0135 - val_accuracy: 0.0037 - lr: 0.0010 - 277ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.01348
48/48 - 0s - loss: 0.0279 - accuracy: 0.0000e+00 - val_loss: 0.0797 - val_accuracy: 0.0037 - lr: 0.0010 - 275ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01348
48/48 - 0s - loss: 0.0309 - accuracy: 0.0000e+00 - val_loss: 0.0415 - val_accuracy: 0.0037 - lr: 0.0010 - 264ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.01348
48/48 - 0s - loss: 0.0315 - accuracy: 0.0000e+00 - val_loss: 0.0942 - val_accuracy: 0.0037 - lr: 0.0010 - 302ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01348
48/48 - 0s - loss: 0.0188 - accuracy: 0.0000e+00 - val_loss: 0.0229 - val_accuracy: 0.0037 - lr: 0.0010 - 247ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.01348
48/48 - 0s - loss: 0.0113 - accuracy: 0.0000e+00 - val_loss: 0.0192 - val_accuracy: 0.0037 - lr: 0.0010 - 270ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.01348 to 0.00813, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0084 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 307ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.00813 to 0.00673, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0067 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 299ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.00673 to 0.00586, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 263ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.00586 to 0.00530, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 307ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.00530 to 0.00495, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 287ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.00495 to 0.00474, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 268ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.00474 to 0.00460, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 290ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss improved from 0.00460 to 0.00452, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 302ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.00452 to 0.00446, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 297ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.00446 to 0.00442, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 263ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.00442 to 0.00440, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 304ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.00440 to 0.00438, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 295ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss improved from 0.00438 to 0.00438, saving model to LSTM2.h5
48/48 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 310ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00021: val_loss did not improve from 0.00438
48/48 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 294ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.5672e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.5419e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.5241e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.5093e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00026: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4956e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 259ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4821e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4686e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4549e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 291ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4411e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4272e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 282ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.4131e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3989e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3845e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3700e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3554e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3407e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3259e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.3110e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2961e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2810e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2659e-04 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 260ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2506e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2353e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 292ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2200e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 247ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.2045e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 260ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.1890e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.1735e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.1578e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.1421e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.1264e-04 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.1105e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.0947e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.0787e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.0627e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 302ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.0466e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.0305e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00438
48/48 - 0s - loss: 9.0143e-04 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.9980e-04 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.9817e-04 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 276ms/epoch - 6ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.9653e-04 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.9488e-04 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 309ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.9323e-04 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.9156e-04 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 293ms/epoch - 6ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.8989e-04 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 293ms/epoch - 6ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.8821e-04 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.8652e-04 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 5ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.8483e-04 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 276ms/epoch - 6ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.8312e-04 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 276ms/epoch - 6ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.00438
48/48 - 0s - loss: 8.8141e-04 - accuracy: 0.0000e+00 - val_loss: 0.0050 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 6ms/step
Epoch 00070: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.40 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4231.556, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3761.238, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.30 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3532.227, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3394.496, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.84 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.62 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3396.496, Time=0.20 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.590 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1693.248
Date:                Sun, 12 Dec 2021   AIC                           3394.496
Time:                        14:41:12   BIC                           3413.260
Sample:                             0   HQIC                          3401.702
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.569      0.000      -1.204      -1.192
ar.L2         -0.8976      0.006   -139.811      0.000      -0.910      -0.885
ar.L3         -0.3984      0.006    -68.662      0.000      -0.410      -0.387
sigma2         3.9230      0.018    215.372      0.000       3.887       3.959
===================================================================================
Ljung-Box (L1) (Q):                  14.54   Jarque-Bera (JB):           2462173.05
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.82
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.11100, saving model to LSTM2.h5
16/16 - 3s - loss: 0.1653 - accuracy: 0.0000e+00 - val_loss: 0.1110 - val_accuracy: 0.0037 - lr: 0.0010 - 3s/epoch - 214ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.11100 to 0.06946, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0510 - accuracy: 0.0000e+00 - val_loss: 0.0695 - val_accuracy: 0.0037 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.06946 to 0.00950, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0063 - accuracy: 0.0000e+00 - val_loss: 0.0095 - val_accuracy: 0.0037 - lr: 0.0010 - 128ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.00950 to 0.00842, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0035 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 0.0010 - 118ms/epoch - 7ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.00842 to 0.00799, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0067 - accuracy: 0.0000e+00 - val_loss: 0.0080 - val_accuracy: 0.0037 - lr: 0.0010 - 119ms/epoch - 7ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 0.0010 - 106ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0031 - accuracy: 0.0000e+00 - val_loss: 0.0148 - val_accuracy: 0.0037 - lr: 0.0010 - 102ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0063 - accuracy: 0.0000e+00 - val_loss: 0.0208 - val_accuracy: 0.0037 - lr: 0.0010 - 103ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0062 - accuracy: 0.0000e+00 - val_loss: 0.0080 - val_accuracy: 0.0037 - lr: 0.0010 - 104ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00010: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0109 - accuracy: 0.0000e+00 - val_loss: 0.0122 - val_accuracy: 0.0037 - lr: 0.0010 - 107ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.00799 to 0.00723, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0160 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 125ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.00723 to 0.00560, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0062 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 124ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.00560 to 0.00528, saving model to LSTM2.h5
16/16 - 0s - loss: 0.0038 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 116ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0035 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 121ms/epoch - 8ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0027 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 114ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0062 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 97ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0064 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 100ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00018: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 96ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 23/500

Epoch 00023: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00023: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00528
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 00063: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.40 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4264.089, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3793.930, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.23 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3564.923, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3427.258, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.20 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.45 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3429.258, Time=0.18 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.686 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1709.629
Date:                Sun, 12 Dec 2021   AIC                           3427.258
Time:                        14:42:30   BIC                           3446.021
Sample:                             0   HQIC                          3434.464
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1981      0.003   -389.386      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.699      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.737      0.000      -0.410      -0.387
sigma2         4.0860      0.019    215.311      0.000       4.049       4.123
===================================================================================
Ljung-Box (L1) (Q):                  14.57   Jarque-Bera (JB):           2460901.70
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.20292, saving model to LSTM2.h5
17/17 - 4s - loss: 0.1103 - accuracy: 0.0000e+00 - val_loss: 0.2029 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 4s/epoch - 210ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.20292 to 0.11069, saving model to LSTM2.h5
17/17 - 0s - loss: 0.1639 - accuracy: 0.0000e+00 - val_loss: 0.1107 - val_accuracy: 0.0037 - lr: 0.0010 - 119ms/epoch - 7ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.11069 to 0.05673, saving model to LSTM2.h5
17/17 - 0s - loss: 0.1197 - accuracy: 0.0000e+00 - val_loss: 0.0567 - val_accuracy: 0.0037 - lr: 0.0010 - 129ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05673
17/17 - 0s - loss: 0.0248 - accuracy: 0.0000e+00 - val_loss: 0.0785 - val_accuracy: 0.0037 - lr: 0.0010 - 110ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.05673 to 0.00858, saving model to LSTM2.h5
17/17 - 0s - loss: 0.0488 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 0.0010 - 128ms/epoch - 8ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00858
17/17 - 0s - loss: 0.0060 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 0.0010 - 105ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00858
17/17 - 0s - loss: 0.0107 - accuracy: 0.0000e+00 - val_loss: 0.0110 - val_accuracy: 0.0037 - lr: 0.0010 - 100ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00858
17/17 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0094 - val_accuracy: 0.0037 - lr: 0.0010 - 116ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.00858 to 0.00558, saving model to LSTM2.h5
17/17 - 0s - loss: 0.0031 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 0.0010 - 143ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00558
17/17 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 0.0010 - 108ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00558
17/17 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 0.0010 - 116ms/epoch - 7ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00558
17/17 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 0.0010 - 107ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.00558 to 0.00475, saving model to LSTM2.h5
17/17 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 0.0010 - 135ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00475
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 0.0010 - 110ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00475
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00475
17/17 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00475
17/17 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0055 - val_accuracy: 0.0037 - lr: 0.0010 - 111ms/epoch - 7ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.00475 to 0.00452, saving model to LSTM2.h5
17/17 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 0.0010 - 130ms/epoch - 8ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.00452 to 0.00443, saving model to LSTM2.h5
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 0.0010 - 142ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 0.0010 - 94ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 0.0010 - 112ms/epoch - 7ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 0.0010 - 110ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00023: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0087 - val_accuracy: 0.0037 - lr: 0.0010 - 105ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 106ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 111ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00443
17/17 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 118ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00443
17/17 - 0s - loss: 9.7258e-04 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 107ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00028: val_loss did not improve from 0.00443
17/17 - 0s - loss: 9.1754e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 115ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.8722e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 121ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.8510e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.8283e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.8068e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00033: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.7866e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.7673e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.7488e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 120ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.7309e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 125ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.7134e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6964e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6797e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6634e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6474e-04 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6318e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6164e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.6014e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 124ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5867e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 122ms/epoch - 7ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5723e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5582e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5444e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5309e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 123ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5177e-04 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.5048e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4922e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4798e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4677e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4559e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4444e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4331e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4221e-04 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4113e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.4007e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 123ms/epoch - 7ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3904e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 118ms/epoch - 7ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3803e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3704e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 120ms/epoch - 7ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3607e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3512e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3420e-04 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3329e-04 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3240e-04 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.00443
17/17 - 0s - loss: 8.3153e-04 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 00069: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678

WMA
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 78.06346997131263 
RMSE:	 8.835353415190172 
MAPE:	 6.948265794170055
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.43 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4436.126, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3965.317, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3736.589, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3598.951, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.92 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.91 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3600.951, Time=0.18 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.028 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1795.475
Date:                Sun, 12 Dec 2021   AIC                           3598.951
Time:                        14:43:51   BIC                           3617.714
Sample:                             0   HQIC                          3606.157
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1983      0.003   -389.581      0.000      -1.204      -1.192
ar.L2         -0.8973      0.006   -139.732      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.649      0.000      -0.410      -0.387
sigma2         5.0573      0.023    215.292      0.000       5.011       5.103
===================================================================================
Ljung-Box (L1) (Q):                  14.41   Jarque-Bera (JB):           2460553.80
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.89
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.74
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04427, saving model to LSTM2.h5
10/10 - 4s - loss: 0.2331 - accuracy: 0.0000e+00 - val_loss: 0.0443 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 388ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.04427 to 0.02873, saving model to LSTM2.h5
10/10 - 0s - loss: 0.1760 - accuracy: 0.0000e+00 - val_loss: 0.0287 - val_accuracy: 0.0037 - lr: 0.0010 - 93ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.02873
10/10 - 0s - loss: 0.0484 - accuracy: 0.0000e+00 - val_loss: 0.0752 - val_accuracy: 0.0037 - lr: 0.0010 - 83ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.02873
10/10 - 0s - loss: 0.0095 - accuracy: 0.0000e+00 - val_loss: 0.0313 - val_accuracy: 0.0037 - lr: 0.0010 - 79ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.02873 to 0.01131, saving model to LSTM2.h5
10/10 - 0s - loss: 0.0045 - accuracy: 0.0000e+00 - val_loss: 0.0113 - val_accuracy: 0.0037 - lr: 0.0010 - 94ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.01131 to 0.01100, saving model to LSTM2.h5
10/10 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0110 - val_accuracy: 0.0037 - lr: 0.0010 - 95ms/epoch - 10ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0162 - val_accuracy: 0.0037 - lr: 0.0010 - 81ms/epoch - 8ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0209 - val_accuracy: 0.0037 - lr: 0.0010 - 73ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 0.0010 - 75ms/epoch - 7ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0163 - val_accuracy: 0.0037 - lr: 0.0010 - 74ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00011: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0154 - val_accuracy: 0.0037 - lr: 0.0010 - 76ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0157 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 79ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0161 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 96ms/epoch - 10ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0163 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 100ms/epoch - 10ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0165 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 78ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00016: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 82ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 84ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 21/500

Epoch 00021: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00021: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 84ms/epoch - 8ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 85ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 89ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 85ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 91ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0167 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 85ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 90ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 91ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0168 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 83ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 79ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 87ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 87ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 85ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 101ms/epoch - 10ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0170 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0170 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0170 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0170 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.01100
10/10 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0170 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 88ms/epoch - 9ms/step
Epoch 00056: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678

WMA
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 78.06346997131263 
RMSE:	 8.835353415190172 
MAPE:	 6.948265794170055

DEMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 153.59400995187858 
RMSE:	 12.39330504554288 
MAPE:	 11.203775482220726
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.38 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4190.464, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3724.371, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.27 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3494.154, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3357.435, Time=0.10 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.15 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.73 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3359.435, Time=0.22 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.992 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1674.717
Date:                Sun, 12 Dec 2021   AIC                           3357.435
Time:                        14:45:03   BIC                           3376.198
Sample:                             0   HQIC                          3364.641
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1955      0.003   -381.246      0.000      -1.202      -1.189
ar.L2         -0.8964      0.007   -135.835      0.000      -0.909      -0.883
ar.L3         -0.3971      0.006    -67.229      0.000      -0.409      -0.385
sigma2         3.7466      0.018    211.623      0.000       3.712       3.781
===================================================================================
Ljung-Box (L1) (Q):                  14.20   Jarque-Bera (JB):           2338363.32
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             3.76
Prob(H) (two-sided):                  0.00   Kurtosis:                       266.93
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.10383, saving model to LSTM2.h5
45/45 - 4s - loss: 0.1770 - accuracy: 0.0000e+00 - val_loss: 0.1038 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 4s/epoch - 90ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.10383 to 0.01943, saving model to LSTM2.h5
45/45 - 0s - loss: 0.0618 - accuracy: 0.0000e+00 - val_loss: 0.0194 - val_accuracy: 0.0037 - lr: 0.0010 - 266ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.01943
45/45 - 0s - loss: 0.0545 - accuracy: 0.0000e+00 - val_loss: 0.2219 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 286ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01943
45/45 - 0s - loss: 0.0658 - accuracy: 0.0000e+00 - val_loss: 0.0253 - val_accuracy: 0.0037 - lr: 0.0010 - 240ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.01943
45/45 - 0s - loss: 0.0312 - accuracy: 0.0000e+00 - val_loss: 0.1697 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 274ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01943
45/45 - 0s - loss: 0.0233 - accuracy: 0.0000e+00 - val_loss: 0.0298 - val_accuracy: 0.0037 - lr: 0.0010 - 258ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.01943 to 0.01815, saving model to LSTM2.h5
45/45 - 0s - loss: 0.0102 - accuracy: 0.0000e+00 - val_loss: 0.0181 - val_accuracy: 0.0037 - lr: 0.0010 - 287ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01815
45/45 - 0s - loss: 0.0061 - accuracy: 0.0000e+00 - val_loss: 0.0275 - val_accuracy: 0.0037 - lr: 0.0010 - 259ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.01815 to 0.00734, saving model to LSTM2.h5
45/45 - 0s - loss: 0.0049 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 0.0010 - 291ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0027 - accuracy: 0.0000e+00 - val_loss: 0.0410 - val_accuracy: 0.0037 - lr: 0.0010 - 261ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0031 - accuracy: 0.0000e+00 - val_loss: 0.0205 - val_accuracy: 0.0037 - lr: 0.0010 - 295ms/epoch - 7ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0504 - val_accuracy: 0.0037 - lr: 0.0010 - 236ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0031 - accuracy: 0.0000e+00 - val_loss: 0.0254 - val_accuracy: 0.0037 - lr: 0.0010 - 244ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00014: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0518 - val_accuracy: 0.0037 - lr: 0.0010 - 318ms/epoch - 7ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0058 - accuracy: 0.0000e+00 - val_loss: 0.0374 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 270ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0044 - accuracy: 0.0000e+00 - val_loss: 0.0258 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 253ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0028 - accuracy: 0.0000e+00 - val_loss: 0.0207 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 282ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0179 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 285ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00019: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0162 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 278ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0159 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0156 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0154 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0152 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00024: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0150 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0149 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0147 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0146 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 239ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0145 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0144 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0143 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0141 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 237ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0140 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0139 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0138 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0137 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0136 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0135 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0134 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0133 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0132 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0131 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0130 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0129 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0128 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 259ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0127 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0126 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0125 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0124 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0123 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0123 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0122 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0121 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0120 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0119 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0118 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0117 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0116 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 239ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0116 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00734
45/45 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0115 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 5ms/step
Epoch 00059: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678

WMA
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 78.06346997131263 
RMSE:	 8.835353415190172 
MAPE:	 6.948265794170055

DEMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 153.59400995187858 
RMSE:	 12.39330504554288 
MAPE:	 11.203775482220726

KAMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 121.28941541171922 
RMSE:	 11.013147388994629 
MAPE:	 9.175643045864026
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.35 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4212.289, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3747.746, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.26 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3523.401, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3387.759, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.27 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.85 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3389.758, Time=0.20 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.168 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1689.879
Date:                Sun, 12 Dec 2021   AIC                           3387.759
Time:                        14:46:33   BIC                           3406.522
Sample:                             0   HQIC                          3394.964
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1878      0.003   -345.315      0.000      -1.195      -1.181
ar.L2         -0.8876      0.007   -121.809      0.000      -0.902      -0.873
ar.L3         -0.3957      0.007    -60.127      0.000      -0.409      -0.383
sigma2         3.8904      0.020    193.404      0.000       3.851       3.930
===================================================================================
Ljung-Box (L1) (Q):                  13.21   Jarque-Bera (JB):           1659080.01
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.08   Skew:                             3.28
Prob(H) (two-sided):                  0.00   Kurtosis:                       225.31
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.13473, saving model to LSTM2.h5
58/58 - 4s - loss: 0.1760 - accuracy: 0.0000e+00 - val_loss: 0.1347 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 4s/epoch - 64ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.13473 to 0.00527, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0557 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 0.0010 - 334ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.00527
58/58 - 0s - loss: 0.0038 - accuracy: 0.0000e+00 - val_loss: 0.0055 - val_accuracy: 0.0037 - lr: 0.0010 - 329ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.00527 to 0.00478, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 0.0010 - 393ms/epoch - 7ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.00478 to 0.00472, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 0.0010 - 375ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0054 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 0.0010 - 333ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0097 - accuracy: 0.0000e+00 - val_loss: 0.0052 - val_accuracy: 0.0037 - lr: 0.0010 - 307ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0255 - accuracy: 0.0000e+00 - val_loss: 0.0556 - val_accuracy: 0.0037 - lr: 0.0010 - 308ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00009: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0276 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 0.0010 - 320ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0433 - accuracy: 0.0000e+00 - val_loss: 0.0879 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 328ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0147 - accuracy: 0.0000e+00 - val_loss: 0.0407 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 308ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0101 - accuracy: 0.0000e+00 - val_loss: 0.0224 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 346ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0081 - accuracy: 0.0000e+00 - val_loss: 0.0130 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 334ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00014: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0066 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 340ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0048 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 328ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0045 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 357ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0043 - accuracy: 0.0000e+00 - val_loss: 0.0067 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 317ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0042 - accuracy: 0.0000e+00 - val_loss: 0.0064 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 338ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00019: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0041 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 306ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0040 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 312ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0039 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 349ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0038 - accuracy: 0.0000e+00 - val_loss: 0.0055 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 330ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0037 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0036 - accuracy: 0.0000e+00 - val_loss: 0.0052 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 335ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0036 - accuracy: 0.0000e+00 - val_loss: 0.0050 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0035 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 305ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00472
58/58 - 0s - loss: 0.0034 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.00472 to 0.00467, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0033 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 348ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss improved from 0.00467 to 0.00457, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0033 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 350ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss improved from 0.00457 to 0.00448, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0032 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 333ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss improved from 0.00448 to 0.00441, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0031 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 345ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss improved from 0.00441 to 0.00435, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 333ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss improved from 0.00435 to 0.00430, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 360ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss improved from 0.00430 to 0.00427, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0029 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 396ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss improved from 0.00427 to 0.00425, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0028 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 342ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss improved from 0.00425 to 0.00424, saving model to LSTM2.h5
58/58 - 0s - loss: 0.0027 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 352ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0027 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 309ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0026 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 343ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 311ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 294ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0045 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 348ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 318ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0047 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 331ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 308ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0050 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 306ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0052 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 340ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 313ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0055 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 325ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 349ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 325ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 321ms/epoch - 6ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 356ms/epoch - 6ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 328ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 339ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0067 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 342ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 315ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 339ms/epoch - 6ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 325ms/epoch - 6ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 316ms/epoch - 5ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0078 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 376ms/epoch - 6ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0079 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 356ms/epoch - 6ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 315ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0082 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 339ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 324ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0085 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 303ms/epoch - 5ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 328ms/epoch - 6ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0088 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 342ms/epoch - 6ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0089 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 302ms/epoch - 5ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0090 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0090 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 318ms/epoch - 5ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0091 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 328ms/epoch - 6ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 322ms/epoch - 6ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 321ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 325ms/epoch - 6ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 309ms/epoch - 5ms/step
Epoch 84/500

Epoch 00084: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 85/500

Epoch 00085: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 388ms/epoch - 7ms/step
Epoch 86/500

Epoch 00086: val_loss did not improve from 0.00424
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0091 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 327ms/epoch - 6ms/step
Epoch 00086: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678

WMA
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 78.06346997131263 
RMSE:	 8.835353415190172 
MAPE:	 6.948265794170055

DEMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 153.59400995187858 
RMSE:	 12.39330504554288 
MAPE:	 11.203775482220726

KAMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 121.28941541171922 
RMSE:	 11.013147388994629 
MAPE:	 9.175643045864026

MIDPOINT
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 110.09412594018622 
RMSE:	 10.492574800314088 
MAPE:	 8.796456301428389
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4414.515, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3944.062, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.35 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3715.173, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3577.471, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.41 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.59 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3579.471, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.160 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1784.736
Date:                Sun, 12 Dec 2021   AIC                           3577.471
Time:                        14:48:17   BIC                           3596.235
Sample:                             0   HQIC                          3584.677
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.844      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.861      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.862      0.000      -0.410      -0.387
sigma2         4.9242      0.023    215.469      0.000       4.879       4.969
===================================================================================
Ljung-Box (L1) (Q):                  14.55   Jarque-Bera (JB):           2468024.38
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       274.15
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.03602, saving model to LSTM2.h5
43/43 - 4s - loss: 0.1500 - accuracy: 0.0000e+00 - val_loss: 0.0360 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 84ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.03602 to 0.01139, saving model to LSTM2.h5
43/43 - 0s - loss: 0.0309 - accuracy: 0.0000e+00 - val_loss: 0.0114 - val_accuracy: 0.0037 - lr: 0.0010 - 274ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.01139
43/43 - 0s - loss: 0.0364 - accuracy: 0.0000e+00 - val_loss: 0.0897 - val_accuracy: 0.0037 - lr: 0.0010 - 262ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01139
43/43 - 0s - loss: 0.0438 - accuracy: 0.0000e+00 - val_loss: 0.0251 - val_accuracy: 0.0037 - lr: 0.0010 - 252ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.01139
43/43 - 0s - loss: 0.0158 - accuracy: 0.0000e+00 - val_loss: 0.0613 - val_accuracy: 0.0037 - lr: 0.0010 - 274ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01139
43/43 - 0s - loss: 0.0117 - accuracy: 0.0000e+00 - val_loss: 0.0312 - val_accuracy: 0.0037 - lr: 0.0010 - 281ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.01139
43/43 - 0s - loss: 0.0083 - accuracy: 0.0000e+00 - val_loss: 0.0216 - val_accuracy: 0.0037 - lr: 0.0010 - 255ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.01139 to 0.00638, saving model to LSTM2.h5
43/43 - 0s - loss: 0.0146 - accuracy: 0.0000e+00 - val_loss: 0.0064 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 256ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.00638 to 0.00515, saving model to LSTM2.h5
43/43 - 0s - loss: 0.0029 - accuracy: 0.0000e+00 - val_loss: 0.0051 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 265ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.00515 to 0.00491, saving model to LSTM2.h5
43/43 - 0s - loss: 0.0026 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 319ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 236ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 277ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 291ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 237ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00015: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 236ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 292ms/epoch - 7ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00020: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 240ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 259ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 283ms/epoch - 7ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 242ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 295ms/epoch - 7ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 245ms/epoch - 6ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0068 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 277ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0068 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00491
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0068 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 00060: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678

WMA
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 78.06346997131263 
RMSE:	 8.835353415190172 
MAPE:	 6.948265794170055

DEMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 153.59400995187858 
RMSE:	 12.39330504554288 
MAPE:	 11.203775482220726

KAMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 121.28941541171922 
RMSE:	 11.013147388994629 
MAPE:	 9.175643045864026

MIDPOINT
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 110.09412594018622 
RMSE:	 10.492574800314088 
MAPE:	 8.796456301428389

T3
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 225.73907153615718 
RMSE:	 15.024615520410403 
MAPE:	 12.611725131734374
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.44 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4352.703, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3889.412, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.29 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3689.930, Time=0.06 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3574.245, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.16 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.77 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3576.245, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.078 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1783.123
Date:                Sun, 12 Dec 2021   AIC                           3574.245
Time:                        14:49:45   BIC                           3593.008
Sample:                             0   HQIC                          3581.451
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1480      0.004   -302.430      0.000      -1.155      -1.141
ar.L2         -0.8300      0.008    -99.682      0.000      -0.846      -0.814
ar.L3         -0.3687      0.007    -50.527      0.000      -0.383      -0.354
sigma2         4.9055      0.028    175.970      0.000       4.851       4.960
===================================================================================
Ljung-Box (L1) (Q):                  11.61   Jarque-Bera (JB):           1261976.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.16   Skew:                             2.52
Prob(H) (two-sided):                  0.00   Kurtosis:                       196.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.32785, saving model to LSTM2.h5
90/90 - 4s - loss: 0.0549 - accuracy: 0.0000e+00 - val_loss: 0.3279 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 4s/epoch - 45ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.32785 to 0.05194, saving model to LSTM2.h5
90/90 - 1s - loss: 0.0650 - accuracy: 0.0000e+00 - val_loss: 0.0519 - val_accuracy: 0.0037 - lr: 0.0010 - 562ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05194
90/90 - 0s - loss: 0.0748 - accuracy: 0.0000e+00 - val_loss: 0.0536 - val_accuracy: 0.0037 - lr: 0.0010 - 489ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.05194 to 0.00511, saving model to LSTM2.h5
90/90 - 0s - loss: 0.0452 - accuracy: 0.0000e+00 - val_loss: 0.0051 - val_accuracy: 0.0037 - lr: 0.0010 - 482ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0236 - accuracy: 0.0000e+00 - val_loss: 0.0153 - val_accuracy: 0.0037 - lr: 0.0010 - 561ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0182 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 0.0010 - 492ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0196 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 0.0010 - 499ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0200 - accuracy: 0.0000e+00 - val_loss: 0.0336 - val_accuracy: 0.0037 - lr: 0.0010 - 539ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00009: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0246 - accuracy: 0.0000e+00 - val_loss: 0.0255 - val_accuracy: 0.0037 - lr: 0.0010 - 567ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0381 - accuracy: 0.0000e+00 - val_loss: 0.0348 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 482ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0083 - accuracy: 0.0000e+00 - val_loss: 0.0203 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 476ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0068 - accuracy: 0.0000e+00 - val_loss: 0.0155 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 501ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0057 - accuracy: 0.0000e+00 - val_loss: 0.0135 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 475ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00014: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0048 - accuracy: 0.0000e+00 - val_loss: 0.0126 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 489ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0037 - accuracy: 0.0000e+00 - val_loss: 0.0118 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 522ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0035 - accuracy: 0.0000e+00 - val_loss: 0.0112 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 491ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0034 - accuracy: 0.0000e+00 - val_loss: 0.0108 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 565ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0033 - accuracy: 0.0000e+00 - val_loss: 0.0104 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 499ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00019: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0032 - accuracy: 0.0000e+00 - val_loss: 0.0102 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 455ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0032 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 572ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0031 - accuracy: 0.0000e+00 - val_loss: 0.0098 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 535ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0097 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 558ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0096 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 465ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0029 - accuracy: 0.0000e+00 - val_loss: 0.0094 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 509ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0028 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 455ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0028 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 507ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0027 - accuracy: 0.0000e+00 - val_loss: 0.0091 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 486ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0027 - accuracy: 0.0000e+00 - val_loss: 0.0090 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 581ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0026 - accuracy: 0.0000e+00 - val_loss: 0.0089 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 465ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0088 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 557ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0087 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 473ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 516ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 457ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0085 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 504ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 535ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0083 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 575ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0082 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 467ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 491ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0080 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 541ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0079 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 488ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0078 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 467ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 491ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 468ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 521ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 540ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 550ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 473ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 490ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0068 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 579ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 472ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 523ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 488ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00511
90/90 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0062 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 472ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00511
90/90 - 1s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 560ms/epoch - 6ms/step
Epoch 00054: early stopping
SMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 75.20110458138421 
RMSE:	 8.67185704341257 
MAPE:	 7.0799160587584336

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	44.78% Accuracy
MSE:	 61.82384712230415 
RMSE:	 7.862814198638052 
MAPE:	 6.504666247736678

WMA
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 78.06346997131263 
RMSE:	 8.835353415190172 
MAPE:	 6.948265794170055

DEMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 153.59400995187858 
RMSE:	 12.39330504554288 
MAPE:	 11.203775482220726

KAMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 121.28941541171922 
RMSE:	 11.013147388994629 
MAPE:	 9.175643045864026

MIDPOINT
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 110.09412594018622 
RMSE:	 10.492574800314088 
MAPE:	 8.796456301428389

T3
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 225.73907153615718 
RMSE:	 15.024615520410403 
MAPE:	 12.611725131734374

TEMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 157.04980120863047 
RMSE:	 12.531951213144364 
MAPE:	 11.294114614846999
Runtime: mins: 12.02286981541666

Architecture Used

In [89]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
In [90]:
img = cv2.imread('Experiment2.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[90]:
<matplotlib.image.AxesImage at 0x7fa5d05a45d0>

Model Plots

In [91]:
for i in range(len(list(simulation2.keys()))):
  SIM = list(simulation2.keys())[i]
  plot_train(simulation2,SIM)
  plot_test(simulation2,SIM)
----- Train RMSE for SMA ----- 8.871298903186338
----- Train_MSE_LSTM for SMA ----- 78.69994422967511
----- Train MAE LSTM for SMA ----- 7.764003126570808
----- Test RMSE for SMA----- 8.67185704341257
----- Test_MSE_LSTM for SMA----- 75.20110458138421
----- Test_MAE_LSTM for SMA----- 7.0799160587584336
----- Train RMSE for EMA ----- 10.179800509177445
----- Train_MSE_LSTM for EMA ----- 103.62833840664938
----- Train MAE LSTM for EMA ----- 8.952723233047472
----- Test RMSE for EMA----- 7.862814198638052
----- Test_MSE_LSTM for EMA----- 61.82384712230415
----- Test_MAE_LSTM for EMA----- 6.504666247736678
----- Train RMSE for WMA ----- 10.496291718330035
----- Train_MSE_LSTM for WMA ----- 110.17213983628366
----- Train MAE LSTM for WMA ----- 9.343427695739683
----- Test RMSE for WMA----- 8.835353415190172
----- Test_MSE_LSTM for WMA----- 78.06346997131263
----- Test_MAE_LSTM for WMA----- 6.948265794170055
----- Train RMSE for DEMA ----- 12.115569265976841
----- Train_MSE_LSTM for DEMA ----- 146.7870186386826
----- Train MAE LSTM for DEMA ----- 10.916550872639965
----- Test RMSE for DEMA----- 12.39330504554288
----- Test_MSE_LSTM for DEMA----- 153.59400995187858
----- Test_MAE_LSTM for DEMA----- 11.203775482220726
----- Train RMSE for KAMA ----- 10.558696520648454
----- Train_MSE_LSTM for KAMA ----- 111.48607221515375
----- Train MAE LSTM for KAMA ----- 9.496371790900133
----- Test RMSE for KAMA----- 11.013147388994629
----- Test_MSE_LSTM for KAMA----- 121.28941541171922
----- Test_MAE_LSTM for KAMA----- 9.175643045864026
----- Train RMSE for MIDPOINT ----- 9.458884562735138
----- Train_MSE_LSTM for MIDPOINT ----- 89.47049717114909
----- Train MAE LSTM for MIDPOINT ----- 8.375965095480147
----- Test RMSE for MIDPOINT----- 10.492574800314088
----- Test_MSE_LSTM for MIDPOINT----- 110.09412594018622
----- Test_MAE_LSTM for MIDPOINT----- 8.796456301428389
----- Train RMSE for T3 ----- 12.046185882632866
----- Train_MSE_LSTM for T3 ----- 145.11059431894336
----- Train MAE LSTM for T3 ----- 10.824884583380554
----- Test RMSE for T3----- 15.024615520410403
----- Test_MSE_LSTM for T3----- 225.73907153615718
----- Test_MAE_LSTM for T3----- 12.611725131734374
----- Train RMSE for TEMA ----- 7.429673273539691
----- Train_MSE_LSTM for TEMA ----- 55.20004495154998
----- Train MAE LSTM for TEMA ----- 5.060762752805627
----- Test RMSE for TEMA----- 12.531951213144364
----- Test_MSE_LSTM for TEMA----- 157.04980120863047
----- Test_MAE_LSTM for TEMA----- 11.294114614846999

Univariate Arima Multistep MutiVariate LSTM Hybrid Model Experiment 3

In [92]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det = 20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # # Option 1
    # # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    # model.add(Dense(units=64,activation='relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    # ## Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()


    # # option 2
    # model = Sequential()
    # model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    # model.add(Dense(64))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()




    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    class Double_Tanh(Activation):
        def __init__(self, activation, **kwargs):
            super(Double_Tanh, self).__init__(activation, **kwargs)
            self.__name__ = 'double_tanh'

    def double_tanh(x):
        return (K.tanh(x) * 2)

    get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
        # Model Generation
    model = Sequential()
    #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    model.add(Dense(1))
    model.add(Activation(double_tanh))
    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM3.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(x_train.shape[1], 1)))
    # model.add(LSTM(units=int(lstm_len/2)))
    # model.add(Dense(1, activation='sigmoid'))
    # model.compile(loss='mean_squared_error', optimizer='adam')
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [93]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation3 = {}
    imgfile = 'Experiment3'
    for ma in optimized_period:
              print(ma)
              print(functions[ma])
              print ( int( optimized_period[ma]))
            # if ma == 'SMA':
              low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
              low_vol = low_vol.fillna(0)
              low_vol_data = df['close']
              high_vol = pd.DataFrame()
              df2 = df.copy()
              for i in df2.columns:
                if i in low_vol.columns:
                  high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
              high_vol_data = df['close']
              ## *****************************************************
              # Generate ARIMA and LSTM predictions
              print('\nWorking on ' + ma + ' predictions')
              try:
                print('parameters used : ', train_len, test_len)
                low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima(low_vol,low_vol_data, train_len, test_len)
              except:
                  print('ARIMA error, skipping to next MA type')
                  continue
              Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
              final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
              mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
              rmse_ftr = mse_ftr ** 0.5
              mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
              mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

              final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
              mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
              rmse = mse ** 0.5
              mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              # Generate prediction accuracy
              actual = df['close'].tail(test_len).values
              result_1 = []
              result_2 = []
              for i in range(1, len(final_prediction)):
                  # Compare prediction to previous close price
                  if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                      result_1.append(1)
                  elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                      result_1.append(1)
                  else:
                      result_1.append(0)

                  # Compare prediction to previous prediction
                  if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                      result_2.append(1)
                  elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                      result_2.append(1)
                  else:
                      result_2.append(0)

              accuracy_1 = np.mean(result_1)
              accuracy_2 = np.mean(result_2)

              simulation3[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                            'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                            'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                            'rmse': rmse_ftr, 'mae' : mae_ftr},
                                'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                          'rmse': rmse, 'mae': mae },
                                'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

              # save simulation data here as checkpoint
              with open('simulation3_data.json', 'w') as fp:
                  json.dump(simulation3, fp)

              for ma in simulation3.keys():
                  print('\n' + ma)
                  print('Prediction vs Close:\t\t' + str(round(100*simulation3[ma]['accuracy']['prediction vs close'], 2))
                        + '% Accuracy')
                  print('Prediction vs Prediction:\t' + str(round(100*simulation3[ma]['accuracy']['prediction vs prediction'], 2))
                        + '% Accuracy')
                  print('MSE:\t', simulation3[ma]['final']['mse'],
                        '\nRMSE:\t', simulation3[ma]['final']['rmse'],
                        '\nMAPE:\t', simulation3[ma]['final']['mae'])#,
                        # '\nMAPE:\t', simulation[ma]['final']['mape'])
            # else:
            #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.51 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4157.020, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3687.148, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.19 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3458.651, Time=0.08 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3322.133, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.77 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.82 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3324.133, Time=0.22 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.752 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1657.067
Date:                Sun, 12 Dec 2021   AIC                           3322.133
Time:                        14:57:27   BIC                           3340.897
Sample:                             0   HQIC                          3329.339
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1966      0.003   -387.226      0.000      -1.203      -1.191
ar.L2         -0.8952      0.006   -138.692      0.000      -0.908      -0.883
ar.L3         -0.3968      0.006    -68.284      0.000      -0.408      -0.385
sigma2         3.5858      0.017    214.535      0.000       3.553       3.619
===================================================================================
Ljung-Box (L1) (Q):                  14.47   Jarque-Bera (JB):           2428881.42
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       271.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.32523, saving model to LSTM3.h5
48/48 - 2s - loss: 0.2060 - mse: 0.2060 - mae: 0.3352 - val_loss: 0.3252 - val_mse: 0.3252 - val_mae: 0.5327 - lr: 0.0010 - 2s/epoch - 44ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.32523
48/48 - 0s - loss: 0.0738 - mse: 0.0738 - mae: 0.2252 - val_loss: 0.3958 - val_mse: 0.3958 - val_mae: 0.5963 - lr: 0.0010 - 193ms/epoch - 4ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.32523 to 0.31040, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0327 - mse: 0.0327 - mae: 0.1433 - val_loss: 0.3104 - val_mse: 0.3104 - val_mae: 0.5247 - lr: 0.0010 - 221ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.31040
48/48 - 0s - loss: 0.0236 - mse: 0.0236 - mae: 0.1211 - val_loss: 0.3787 - val_mse: 0.3787 - val_mae: 0.5870 - lr: 0.0010 - 212ms/epoch - 4ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.31040
48/48 - 0s - loss: 0.0182 - mse: 0.0182 - mae: 0.1046 - val_loss: 0.3283 - val_mse: 0.3283 - val_mae: 0.5451 - lr: 0.0010 - 229ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.31040
48/48 - 0s - loss: 0.0143 - mse: 0.0143 - mae: 0.0938 - val_loss: 0.3362 - val_mse: 0.3362 - val_mae: 0.5529 - lr: 0.0010 - 211ms/epoch - 4ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.31040 to 0.27120, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0125 - mse: 0.0125 - mae: 0.0880 - val_loss: 0.2712 - val_mse: 0.2712 - val_mae: 0.4936 - lr: 0.0010 - 260ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.27120
48/48 - 0s - loss: 0.0110 - mse: 0.0110 - mae: 0.0823 - val_loss: 0.3407 - val_mse: 0.3407 - val_mae: 0.5586 - lr: 0.0010 - 230ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.27120 to 0.24048, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0107 - mse: 0.0107 - mae: 0.0812 - val_loss: 0.2405 - val_mse: 0.2405 - val_mae: 0.4655 - lr: 0.0010 - 211ms/epoch - 4ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.24048
48/48 - 0s - loss: 0.0088 - mse: 0.0088 - mae: 0.0728 - val_loss: 0.3159 - val_mse: 0.3159 - val_mae: 0.5381 - lr: 0.0010 - 219ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.24048 to 0.21784, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0107 - mse: 0.0107 - mae: 0.0801 - val_loss: 0.2178 - val_mse: 0.2178 - val_mae: 0.4433 - lr: 0.0010 - 218ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.21784
48/48 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0702 - val_loss: 0.3228 - val_mse: 0.3228 - val_mae: 0.5467 - lr: 0.0010 - 228ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.21784 to 0.16075, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0090 - mse: 0.0090 - mae: 0.0751 - val_loss: 0.1608 - val_mse: 0.1608 - val_mae: 0.3764 - lr: 0.0010 - 248ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.16075
48/48 - 0s - loss: 0.0091 - mse: 0.0091 - mae: 0.0728 - val_loss: 0.3245 - val_mse: 0.3245 - val_mae: 0.5497 - lr: 0.0010 - 215ms/epoch - 4ms/step
Epoch 15/500

Epoch 00015: val_loss improved from 0.16075 to 0.10621, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0120 - mse: 0.0120 - mae: 0.0830 - val_loss: 0.1062 - val_mse: 0.1062 - val_mae: 0.2995 - lr: 0.0010 - 227ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.10621
48/48 - 0s - loss: 0.0146 - mse: 0.0146 - mae: 0.0872 - val_loss: 0.4383 - val_mse: 0.4383 - val_mae: 0.6444 - lr: 0.0010 - 210ms/epoch - 4ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.10621 to 0.03004, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0200 - mse: 0.0200 - mae: 0.1034 - val_loss: 0.0300 - val_mse: 0.0300 - val_mae: 0.1374 - lr: 0.0010 - 254ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03004
48/48 - 0s - loss: 0.0230 - mse: 0.0230 - mae: 0.1172 - val_loss: 0.4537 - val_mse: 0.4537 - val_mae: 0.6558 - lr: 0.0010 - 224ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.03004 to 0.02569, saving model to LSTM3.h5
48/48 - 0s - loss: 0.0254 - mse: 0.0254 - mae: 0.1215 - val_loss: 0.0257 - val_mse: 0.0257 - val_mae: 0.1258 - lr: 0.0010 - 237ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0249 - mse: 0.0249 - mae: 0.1271 - val_loss: 0.2785 - val_mse: 0.2785 - val_mae: 0.5097 - lr: 0.0010 - 242ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0161 - mse: 0.0161 - mae: 0.0976 - val_loss: 0.0512 - val_mse: 0.0512 - val_mae: 0.2018 - lr: 0.0010 - 247ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0134 - mse: 0.0134 - mae: 0.0941 - val_loss: 0.2064 - val_mse: 0.2064 - val_mae: 0.4377 - lr: 0.0010 - 226ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0090 - mse: 0.0090 - mae: 0.0746 - val_loss: 0.0759 - val_mse: 0.0759 - val_mae: 0.2555 - lr: 0.0010 - 219ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00024: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0089 - mse: 0.0089 - mae: 0.0760 - val_loss: 0.1579 - val_mse: 0.1579 - val_mae: 0.3803 - lr: 0.0010 - 277ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0103 - mse: 0.0103 - mae: 0.0805 - val_loss: 0.1393 - val_mse: 0.1393 - val_mae: 0.3563 - lr: 1.0000e-04 - 213ms/epoch - 4ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0575 - val_loss: 0.1345 - val_mse: 0.1345 - val_mae: 0.3497 - lr: 1.0000e-04 - 231ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0585 - val_loss: 0.1325 - val_mse: 0.1325 - val_mae: 0.3469 - lr: 1.0000e-04 - 190ms/epoch - 4ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0550 - val_loss: 0.1280 - val_mse: 0.1280 - val_mae: 0.3404 - lr: 1.0000e-04 - 265ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00029: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0554 - val_loss: 0.1241 - val_mse: 0.1241 - val_mae: 0.3348 - lr: 1.0000e-04 - 234ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0536 - val_loss: 0.1242 - val_mse: 0.1242 - val_mae: 0.3350 - lr: 1.0000e-05 - 198ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0528 - val_loss: 0.1242 - val_mse: 0.1242 - val_mae: 0.3350 - lr: 1.0000e-05 - 203ms/epoch - 4ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0536 - val_loss: 0.1243 - val_mse: 0.1243 - val_mae: 0.3352 - lr: 1.0000e-05 - 238ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0526 - val_loss: 0.1242 - val_mse: 0.1242 - val_mae: 0.3349 - lr: 1.0000e-05 - 213ms/epoch - 4ms/step
Epoch 34/500

Epoch 00034: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00034: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0525 - val_loss: 0.1239 - val_mse: 0.1239 - val_mae: 0.3346 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0525 - val_loss: 0.1238 - val_mse: 0.1238 - val_mae: 0.3344 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0520 - val_loss: 0.1239 - val_mse: 0.1239 - val_mae: 0.3345 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0542 - val_loss: 0.1234 - val_mse: 0.1234 - val_mae: 0.3338 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0534 - val_loss: 0.1232 - val_mse: 0.1232 - val_mae: 0.3336 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0538 - val_loss: 0.1230 - val_mse: 0.1230 - val_mae: 0.3333 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0523 - val_loss: 0.1227 - val_mse: 0.1227 - val_mae: 0.3328 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0552 - val_loss: 0.1222 - val_mse: 0.1222 - val_mae: 0.3321 - lr: 1.0000e-05 - 250ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0534 - val_loss: 0.1219 - val_mse: 0.1219 - val_mae: 0.3316 - lr: 1.0000e-05 - 203ms/epoch - 4ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0497 - val_loss: 0.1215 - val_mse: 0.1215 - val_mae: 0.3310 - lr: 1.0000e-05 - 213ms/epoch - 4ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0530 - val_loss: 0.1214 - val_mse: 0.1214 - val_mae: 0.3310 - lr: 1.0000e-05 - 202ms/epoch - 4ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0521 - val_loss: 0.1215 - val_mse: 0.1215 - val_mae: 0.3311 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0503 - val_loss: 0.1214 - val_mse: 0.1214 - val_mae: 0.3309 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0531 - val_loss: 0.1215 - val_mse: 0.1215 - val_mae: 0.3311 - lr: 1.0000e-05 - 207ms/epoch - 4ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0514 - val_loss: 0.1218 - val_mse: 0.1218 - val_mae: 0.3315 - lr: 1.0000e-05 - 209ms/epoch - 4ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.1214 - val_mse: 0.1214 - val_mae: 0.3309 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0541 - val_loss: 0.1213 - val_mse: 0.1213 - val_mae: 0.3308 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0514 - val_loss: 0.1213 - val_mse: 0.1213 - val_mae: 0.3308 - lr: 1.0000e-05 - 199ms/epoch - 4ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0512 - val_loss: 0.1209 - val_mse: 0.1209 - val_mae: 0.3302 - lr: 1.0000e-05 - 238ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0504 - val_loss: 0.1204 - val_mse: 0.1204 - val_mae: 0.3295 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0526 - val_loss: 0.1201 - val_mse: 0.1201 - val_mae: 0.3290 - lr: 1.0000e-05 - 238ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0522 - val_loss: 0.1200 - val_mse: 0.1200 - val_mae: 0.3289 - lr: 1.0000e-05 - 207ms/epoch - 4ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0515 - val_loss: 0.1203 - val_mse: 0.1203 - val_mae: 0.3293 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0519 - val_loss: 0.1200 - val_mse: 0.1200 - val_mae: 0.3290 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0509 - val_loss: 0.1202 - val_mse: 0.1202 - val_mae: 0.3293 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0502 - val_loss: 0.1209 - val_mse: 0.1209 - val_mae: 0.3303 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0527 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3298 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0501 - val_loss: 0.1202 - val_mse: 0.1202 - val_mae: 0.3293 - lr: 1.0000e-05 - 206ms/epoch - 4ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0500 - val_loss: 0.1200 - val_mse: 0.1200 - val_mae: 0.3290 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0480 - val_loss: 0.1194 - val_mse: 0.1194 - val_mae: 0.3281 - lr: 1.0000e-05 - 257ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0533 - val_loss: 0.1189 - val_mse: 0.1189 - val_mae: 0.3274 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0505 - val_loss: 0.1182 - val_mse: 0.1182 - val_mae: 0.3264 - lr: 1.0000e-05 - 254ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0496 - val_loss: 0.1185 - val_mse: 0.1185 - val_mae: 0.3269 - lr: 1.0000e-05 - 205ms/epoch - 4ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0498 - val_loss: 0.1183 - val_mse: 0.1183 - val_mae: 0.3265 - lr: 1.0000e-05 - 209ms/epoch - 4ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.1178 - val_mse: 0.1178 - val_mae: 0.3257 - lr: 1.0000e-05 - 209ms/epoch - 4ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.02569
48/48 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0498 - val_loss: 0.1180 - val_mse: 0.1180 - val_mae: 0.3261 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 00069: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.41 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4231.556, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3761.238, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.27 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3532.227, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3394.496, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.87 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.62 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3396.496, Time=0.21 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.621 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1693.248
Date:                Sun, 12 Dec 2021   AIC                           3394.496
Time:                        14:58:57   BIC                           3413.260
Sample:                             0   HQIC                          3401.702
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.569      0.000      -1.204      -1.192
ar.L2         -0.8976      0.006   -139.811      0.000      -0.910      -0.885
ar.L3         -0.3984      0.006    -68.662      0.000      -0.410      -0.387
sigma2         3.9230      0.018    215.372      0.000       3.887       3.959
===================================================================================
Ljung-Box (L1) (Q):                  14.54   Jarque-Bera (JB):           2462173.05
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.82
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.15984, saving model to LSTM3.h5
16/16 - 2s - loss: 0.1221 - mse: 0.1221 - mae: 0.2728 - val_loss: 0.1598 - val_mse: 0.1598 - val_mae: 0.3529 - lr: 0.0010 - 2s/epoch - 156ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.15984 to 0.11744, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0788 - mse: 0.0788 - mae: 0.2263 - val_loss: 0.1174 - val_mse: 0.1174 - val_mae: 0.2992 - lr: 0.0010 - 93ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.11744 to 0.08186, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0321 - mse: 0.0321 - mae: 0.1422 - val_loss: 0.0819 - val_mse: 0.0819 - val_mae: 0.2447 - lr: 0.0010 - 97ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.08186 to 0.05965, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0213 - mse: 0.0213 - mae: 0.1188 - val_loss: 0.0596 - val_mse: 0.0596 - val_mae: 0.2041 - lr: 0.0010 - 92ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.05965 to 0.05652, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0213 - mse: 0.0213 - mae: 0.1151 - val_loss: 0.0565 - val_mse: 0.0565 - val_mae: 0.1988 - lr: 0.0010 - 103ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.05652 to 0.04919, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0131 - mse: 0.0131 - mae: 0.0893 - val_loss: 0.0492 - val_mse: 0.0492 - val_mae: 0.1834 - lr: 0.0010 - 104ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.04919 to 0.04071, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0124 - mse: 0.0124 - mae: 0.0874 - val_loss: 0.0407 - val_mse: 0.0407 - val_mae: 0.1639 - lr: 0.0010 - 106ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.04071 to 0.03576, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0099 - mse: 0.0099 - mae: 0.0778 - val_loss: 0.0358 - val_mse: 0.0358 - val_mae: 0.1518 - lr: 0.0010 - 105ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.03576
16/16 - 0s - loss: 0.0111 - mse: 0.0111 - mae: 0.0812 - val_loss: 0.0411 - val_mse: 0.0411 - val_mae: 0.1654 - lr: 0.0010 - 91ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.03576
16/16 - 0s - loss: 0.0097 - mse: 0.0097 - mae: 0.0784 - val_loss: 0.0360 - val_mse: 0.0360 - val_mae: 0.1526 - lr: 0.0010 - 80ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.03576 to 0.03549, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0091 - mse: 0.0091 - mae: 0.0742 - val_loss: 0.0355 - val_mse: 0.0355 - val_mae: 0.1519 - lr: 0.0010 - 107ms/epoch - 7ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0086 - mse: 0.0086 - mae: 0.0721 - val_loss: 0.0411 - val_mse: 0.0411 - val_mae: 0.1674 - lr: 0.0010 - 88ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0675 - val_loss: 0.0394 - val_mse: 0.0394 - val_mae: 0.1635 - lr: 0.0010 - 79ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0670 - val_loss: 0.0397 - val_mse: 0.0397 - val_mae: 0.1653 - lr: 0.0010 - 86ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0654 - val_loss: 0.0361 - val_mse: 0.0361 - val_mae: 0.1563 - lr: 0.0010 - 82ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00016: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0648 - val_loss: 0.0424 - val_mse: 0.0424 - val_mae: 0.1744 - lr: 0.0010 - 87ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0627 - val_loss: 0.0419 - val_mse: 0.0419 - val_mae: 0.1733 - lr: 1.0000e-04 - 96ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0632 - val_loss: 0.0410 - val_mse: 0.0410 - val_mae: 0.1711 - lr: 1.0000e-04 - 94ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0645 - val_loss: 0.0397 - val_mse: 0.0397 - val_mae: 0.1678 - lr: 1.0000e-04 - 79ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0624 - val_loss: 0.0391 - val_mse: 0.0391 - val_mae: 0.1660 - lr: 1.0000e-04 - 78ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00021: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0644 - val_loss: 0.0389 - val_mse: 0.0389 - val_mae: 0.1658 - lr: 1.0000e-04 - 90ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0606 - val_loss: 0.0388 - val_mse: 0.0388 - val_mae: 0.1655 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0627 - val_loss: 0.0387 - val_mse: 0.0387 - val_mae: 0.1652 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0638 - val_loss: 0.0386 - val_mse: 0.0386 - val_mae: 0.1650 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0600 - val_loss: 0.0386 - val_mse: 0.0386 - val_mae: 0.1647 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00026: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0589 - val_loss: 0.0384 - val_mse: 0.0384 - val_mae: 0.1644 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0589 - val_loss: 0.0384 - val_mse: 0.0384 - val_mae: 0.1643 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0623 - val_loss: 0.0384 - val_mse: 0.0384 - val_mae: 0.1642 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0606 - val_loss: 0.0383 - val_mse: 0.0383 - val_mae: 0.1640 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0595 - val_loss: 0.0381 - val_mse: 0.0381 - val_mae: 0.1636 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0633 - val_loss: 0.0382 - val_mse: 0.0382 - val_mae: 0.1637 - lr: 1.0000e-05 - 131ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0637 - val_loss: 0.0381 - val_mse: 0.0381 - val_mae: 0.1635 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0591 - val_loss: 0.0379 - val_mse: 0.0379 - val_mae: 0.1631 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0639 - val_loss: 0.0379 - val_mse: 0.0379 - val_mae: 0.1629 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0600 - val_loss: 0.0378 - val_mse: 0.0378 - val_mae: 0.1628 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0627 - val_loss: 0.0377 - val_mse: 0.0377 - val_mae: 0.1624 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0624 - val_loss: 0.0376 - val_mse: 0.0376 - val_mae: 0.1621 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0596 - val_loss: 0.0374 - val_mse: 0.0374 - val_mae: 0.1618 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0617 - val_loss: 0.0374 - val_mse: 0.0374 - val_mae: 0.1616 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0627 - val_loss: 0.0373 - val_mse: 0.0373 - val_mae: 0.1613 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0633 - val_loss: 0.0372 - val_mse: 0.0372 - val_mae: 0.1610 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0607 - val_loss: 0.0370 - val_mse: 0.0370 - val_mae: 0.1606 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0602 - val_loss: 0.0369 - val_mse: 0.0369 - val_mae: 0.1604 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0610 - val_loss: 0.0369 - val_mse: 0.0369 - val_mae: 0.1603 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0629 - val_loss: 0.0368 - val_mse: 0.0368 - val_mae: 0.1602 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0616 - val_loss: 0.0367 - val_mse: 0.0367 - val_mae: 0.1598 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0608 - val_loss: 0.0365 - val_mse: 0.0365 - val_mae: 0.1593 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0595 - val_loss: 0.0364 - val_mse: 0.0364 - val_mae: 0.1589 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0621 - val_loss: 0.0362 - val_mse: 0.0362 - val_mae: 0.1585 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0637 - val_loss: 0.0361 - val_mse: 0.0361 - val_mae: 0.1582 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0589 - val_loss: 0.0361 - val_mse: 0.0361 - val_mae: 0.1580 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0591 - val_loss: 0.0360 - val_mse: 0.0360 - val_mae: 0.1578 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0608 - val_loss: 0.0359 - val_mse: 0.0359 - val_mae: 0.1575 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0620 - val_loss: 0.0359 - val_mse: 0.0359 - val_mae: 0.1575 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0618 - val_loss: 0.0358 - val_mse: 0.0358 - val_mae: 0.1573 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0615 - val_loss: 0.0357 - val_mse: 0.0357 - val_mae: 0.1570 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0628 - val_loss: 0.0356 - val_mse: 0.0356 - val_mae: 0.1567 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.03549
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0619 - val_loss: 0.0355 - val_mse: 0.0355 - val_mae: 0.1565 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss improved from 0.03549 to 0.03541, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0616 - val_loss: 0.0354 - val_mse: 0.0354 - val_mae: 0.1562 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss improved from 0.03541 to 0.03537, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0584 - val_loss: 0.0354 - val_mse: 0.0354 - val_mae: 0.1561 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 61/500

Epoch 00061: val_loss improved from 0.03537 to 0.03535, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0631 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1561 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 62/500

Epoch 00062: val_loss improved from 0.03535 to 0.03528, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0583 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1559 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss improved from 0.03528 to 0.03526, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0563 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1559 - lr: 1.0000e-05 - 127ms/epoch - 8ms/step
Epoch 64/500

Epoch 00064: val_loss improved from 0.03526 to 0.03525, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0621 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1558 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 65/500

Epoch 00065: val_loss improved from 0.03525 to 0.03522, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0628 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1558 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.03522
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0594 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1559 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.03522
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0634 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1561 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.03522
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0619 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1561 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.03522
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0617 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1559 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.03522
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0632 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1559 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 71/500

Epoch 00071: val_loss improved from 0.03522 to 0.03518, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0611 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1558 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 72/500

Epoch 00072: val_loss improved from 0.03518 to 0.03514, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0576 - val_loss: 0.0351 - val_mse: 0.0351 - val_mae: 0.1557 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.03514
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0599 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1557 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 74/500

Epoch 00074: val_loss improved from 0.03514 to 0.03513, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0625 - val_loss: 0.0351 - val_mse: 0.0351 - val_mae: 0.1557 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 75/500

Epoch 00075: val_loss improved from 0.03513 to 0.03511, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0577 - val_loss: 0.0351 - val_mse: 0.0351 - val_mae: 0.1557 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.03511
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0619 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1559 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.03511
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0596 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1559 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.03511
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0605 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1562 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.03511
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0603 - val_loss: 0.0351 - val_mse: 0.0351 - val_mae: 0.1559 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 80/500

Epoch 00080: val_loss improved from 0.03511 to 0.03500, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0588 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1554 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.03500
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0609 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1556 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 82/500

Epoch 00082: val_loss improved from 0.03500 to 0.03495, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0572 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1553 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 83/500

Epoch 00083: val_loss improved from 0.03495 to 0.03491, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0576 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1552 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 84/500

Epoch 00084: val_loss improved from 0.03491 to 0.03489, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0601 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1551 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 85/500

Epoch 00085: val_loss improved from 0.03489 to 0.03488, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0596 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1551 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 86/500

Epoch 00086: val_loss did not improve from 0.03488
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0611 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1553 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 87/500

Epoch 00087: val_loss did not improve from 0.03488
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0582 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1553 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 88/500

Epoch 00088: val_loss improved from 0.03488 to 0.03485, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0608 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1551 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 89/500

Epoch 00089: val_loss did not improve from 0.03485
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0579 - val_loss: 0.0349 - val_mse: 0.0349 - val_mae: 0.1552 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 90/500

Epoch 00090: val_loss improved from 0.03485 to 0.03481, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0580 - val_loss: 0.0348 - val_mse: 0.0348 - val_mae: 0.1550 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 91/500

Epoch 00091: val_loss improved from 0.03481 to 0.03474, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0612 - val_loss: 0.0347 - val_mse: 0.0347 - val_mae: 0.1548 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 92/500

Epoch 00092: val_loss improved from 0.03474 to 0.03465, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0599 - val_loss: 0.0347 - val_mse: 0.0347 - val_mae: 0.1546 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 93/500

Epoch 00093: val_loss improved from 0.03465 to 0.03462, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0594 - val_loss: 0.0346 - val_mse: 0.0346 - val_mae: 0.1545 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 94/500

Epoch 00094: val_loss did not improve from 0.03462
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0626 - val_loss: 0.0347 - val_mse: 0.0347 - val_mae: 0.1547 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 95/500

Epoch 00095: val_loss improved from 0.03462 to 0.03459, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0584 - val_loss: 0.0346 - val_mse: 0.0346 - val_mae: 0.1545 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 96/500

Epoch 00096: val_loss improved from 0.03459 to 0.03446, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0586 - val_loss: 0.0345 - val_mse: 0.0345 - val_mae: 0.1541 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 97/500

Epoch 00097: val_loss did not improve from 0.03446
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0577 - val_loss: 0.0345 - val_mse: 0.0345 - val_mae: 0.1542 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 98/500

Epoch 00098: val_loss did not improve from 0.03446
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0609 - val_loss: 0.0345 - val_mse: 0.0345 - val_mae: 0.1543 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 99/500

Epoch 00099: val_loss improved from 0.03446 to 0.03436, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0541 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1539 - lr: 1.0000e-05 - 151ms/epoch - 9ms/step
Epoch 100/500

Epoch 00100: val_loss improved from 0.03436 to 0.03424, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0569 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1536 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 101/500

Epoch 00101: val_loss improved from 0.03424 to 0.03420, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0594 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1534 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 102/500

Epoch 00102: val_loss improved from 0.03420 to 0.03407, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0604 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1531 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 103/500

Epoch 00103: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0612 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1534 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 104/500

Epoch 00104: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0594 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1533 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 105/500

Epoch 00105: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0644 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1533 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 106/500

Epoch 00106: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0632 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1534 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 107/500

Epoch 00107: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0609 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1533 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 108/500

Epoch 00108: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0601 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1536 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 109/500

Epoch 00109: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0610 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1541 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 110/500

Epoch 00110: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0583 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1541 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 111/500

Epoch 00111: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0580 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1543 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 112/500

Epoch 00112: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0612 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1544 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 113/500

Epoch 00113: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0591 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1543 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 114/500

Epoch 00114: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0599 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1544 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 115/500

Epoch 00115: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0601 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1540 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 116/500

Epoch 00116: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0609 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1537 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 117/500

Epoch 00117: val_loss did not improve from 0.03407
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0568 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1535 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 118/500

Epoch 00118: val_loss improved from 0.03407 to 0.03398, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0612 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1532 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 119/500

Epoch 00119: val_loss improved from 0.03398 to 0.03383, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0609 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1527 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 120/500

Epoch 00120: val_loss improved from 0.03383 to 0.03373, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0586 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1524 - lr: 1.0000e-05 - 123ms/epoch - 8ms/step
Epoch 121/500

Epoch 00121: val_loss improved from 0.03373 to 0.03350, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0592 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1518 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 122/500

Epoch 00122: val_loss improved from 0.03350 to 0.03347, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0593 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1517 - lr: 1.0000e-05 - 126ms/epoch - 8ms/step
Epoch 123/500

Epoch 00123: val_loss improved from 0.03347 to 0.03329, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0621 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1512 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 124/500

Epoch 00124: val_loss improved from 0.03329 to 0.03309, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0597 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1506 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 125/500

Epoch 00125: val_loss improved from 0.03309 to 0.03303, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0594 - val_loss: 0.0330 - val_mse: 0.0330 - val_mae: 0.1504 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 126/500

Epoch 00126: val_loss did not improve from 0.03303
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0569 - val_loss: 0.0330 - val_mse: 0.0330 - val_mae: 0.1505 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 127/500

Epoch 00127: val_loss improved from 0.03303 to 0.03293, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0607 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1501 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 128/500

Epoch 00128: val_loss improved from 0.03293 to 0.03284, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0602 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1499 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 129/500

Epoch 00129: val_loss improved from 0.03284 to 0.03272, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0577 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1495 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 130/500

Epoch 00130: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0577 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1496 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 131/500

Epoch 00131: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0610 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1501 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 132/500

Epoch 00132: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0592 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1506 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 133/500

Epoch 00133: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0599 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1510 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 134/500

Epoch 00134: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0609 - val_loss: 0.0330 - val_mse: 0.0330 - val_mae: 0.1505 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 135/500

Epoch 00135: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0570 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1502 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 136/500

Epoch 00136: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0595 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1502 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 137/500

Epoch 00137: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0581 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1502 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 138/500

Epoch 00138: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0600 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1497 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 139/500

Epoch 00139: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0613 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1499 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 140/500

Epoch 00140: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0604 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1501 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 141/500

Epoch 00141: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0583 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1502 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 142/500

Epoch 00142: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0564 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1502 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 143/500

Epoch 00143: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0590 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1501 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 144/500

Epoch 00144: val_loss did not improve from 0.03272
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0568 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1500 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 145/500

Epoch 00145: val_loss improved from 0.03272 to 0.03264, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0591 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1496 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 146/500

Epoch 00146: val_loss improved from 0.03264 to 0.03260, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0568 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1495 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 147/500

Epoch 00147: val_loss improved from 0.03260 to 0.03258, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0565 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1495 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 148/500

Epoch 00148: val_loss improved from 0.03258 to 0.03241, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0555 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1490 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 149/500

Epoch 00149: val_loss improved from 0.03241 to 0.03226, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0561 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1485 - lr: 1.0000e-05 - 145ms/epoch - 9ms/step
Epoch 150/500

Epoch 00150: val_loss improved from 0.03226 to 0.03221, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0590 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1484 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 151/500

Epoch 00151: val_loss improved from 0.03221 to 0.03215, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0566 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1482 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 152/500

Epoch 00152: val_loss improved from 0.03215 to 0.03194, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0571 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1476 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 153/500

Epoch 00153: val_loss improved from 0.03194 to 0.03176, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0578 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1471 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 154/500

Epoch 00154: val_loss improved from 0.03176 to 0.03169, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0565 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1469 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 155/500

Epoch 00155: val_loss improved from 0.03169 to 0.03159, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0590 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1466 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 156/500

Epoch 00156: val_loss improved from 0.03159 to 0.03159, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0563 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1466 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 157/500

Epoch 00157: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0591 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1468 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 158/500

Epoch 00158: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0561 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1472 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 159/500

Epoch 00159: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0594 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1474 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 160/500

Epoch 00160: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0603 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1476 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 161/500

Epoch 00161: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0567 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1477 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 162/500

Epoch 00162: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0575 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1471 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 163/500

Epoch 00163: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0597 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1472 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 164/500

Epoch 00164: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0568 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1474 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 165/500

Epoch 00165: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0578 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1476 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 166/500

Epoch 00166: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0574 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1479 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 167/500

Epoch 00167: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0601 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1484 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 168/500

Epoch 00168: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0568 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1484 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 169/500

Epoch 00169: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0566 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1483 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 170/500

Epoch 00170: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0567 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1487 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 171/500

Epoch 00171: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0574 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1493 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 172/500

Epoch 00172: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0569 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1490 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 173/500

Epoch 00173: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0597 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1493 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 174/500

Epoch 00174: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0565 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1495 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 175/500

Epoch 00175: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0565 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1497 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 176/500

Epoch 00176: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0561 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1498 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 177/500

Epoch 00177: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0567 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1497 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 178/500

Epoch 00178: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0568 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1498 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 179/500

Epoch 00179: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0573 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1495 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 180/500

Epoch 00180: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0579 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1494 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 181/500

Epoch 00181: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0549 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1492 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 182/500

Epoch 00182: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0556 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1486 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 183/500

Epoch 00183: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0563 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1483 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 184/500

Epoch 00184: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0560 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1479 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 185/500

Epoch 00185: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0594 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1484 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 186/500

Epoch 00186: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0556 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1485 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 187/500

Epoch 00187: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0553 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1490 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 188/500

Epoch 00188: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0572 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1490 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 189/500

Epoch 00189: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0544 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1492 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 190/500

Epoch 00190: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0567 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1491 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 191/500

Epoch 00191: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0575 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1491 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 192/500

Epoch 00192: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0536 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1490 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 193/500

Epoch 00193: val_loss did not improve from 0.03159
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0569 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1478 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 194/500

Epoch 00194: val_loss improved from 0.03159 to 0.03145, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0589 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1471 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 195/500

Epoch 00195: val_loss improved from 0.03145 to 0.03107, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0560 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1460 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 196/500

Epoch 00196: val_loss improved from 0.03107 to 0.03085, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0565 - val_loss: 0.0309 - val_mse: 0.0309 - val_mae: 0.1453 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 197/500

Epoch 00197: val_loss did not improve from 0.03085
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0549 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1458 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 198/500

Epoch 00198: val_loss did not improve from 0.03085
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0578 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1463 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 199/500

Epoch 00199: val_loss did not improve from 0.03085
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0561 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1462 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 200/500

Epoch 00200: val_loss did not improve from 0.03085
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0558 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1460 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 201/500

Epoch 00201: val_loss did not improve from 0.03085
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0523 - val_loss: 0.0309 - val_mse: 0.0309 - val_mae: 0.1456 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 202/500

Epoch 00202: val_loss improved from 0.03085 to 0.03069, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0543 - val_loss: 0.0307 - val_mse: 0.0307 - val_mae: 0.1449 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 203/500

Epoch 00203: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0553 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1451 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 204/500

Epoch 00204: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0551 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1453 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 205/500

Epoch 00205: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0562 - val_loss: 0.0307 - val_mse: 0.0307 - val_mae: 0.1450 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 206/500

Epoch 00206: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0555 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1453 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 207/500

Epoch 00207: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0564 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1463 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 208/500

Epoch 00208: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0554 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1466 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 209/500

Epoch 00209: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0569 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1465 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 210/500

Epoch 00210: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0553 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1464 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 211/500

Epoch 00211: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0576 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1464 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 212/500

Epoch 00212: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0579 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1464 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 213/500

Epoch 00213: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0550 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1464 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 214/500

Epoch 00214: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0555 - val_loss: 0.0309 - val_mse: 0.0309 - val_mae: 0.1461 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 215/500

Epoch 00215: val_loss did not improve from 0.03069
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0544 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1455 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 216/500

Epoch 00216: val_loss improved from 0.03069 to 0.03062, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0552 - val_loss: 0.0306 - val_mse: 0.0306 - val_mae: 0.1451 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 217/500

Epoch 00217: val_loss improved from 0.03062 to 0.03053, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0540 - val_loss: 0.0305 - val_mse: 0.0305 - val_mae: 0.1448 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 218/500

Epoch 00218: val_loss improved from 0.03053 to 0.03041, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0546 - val_loss: 0.0304 - val_mse: 0.0304 - val_mae: 0.1445 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 219/500

Epoch 00219: val_loss improved from 0.03041 to 0.03038, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0544 - val_loss: 0.0304 - val_mse: 0.0304 - val_mae: 0.1444 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 220/500

Epoch 00220: val_loss improved from 0.03038 to 0.03010, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0537 - val_loss: 0.0301 - val_mse: 0.0301 - val_mae: 0.1436 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 221/500

Epoch 00221: val_loss improved from 0.03010 to 0.02986, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0552 - val_loss: 0.0299 - val_mse: 0.0299 - val_mae: 0.1428 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 222/500

Epoch 00222: val_loss improved from 0.02986 to 0.02981, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0553 - val_loss: 0.0298 - val_mse: 0.0298 - val_mae: 0.1427 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 223/500

Epoch 00223: val_loss improved from 0.02981 to 0.02975, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0555 - val_loss: 0.0298 - val_mse: 0.0298 - val_mae: 0.1425 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 224/500

Epoch 00224: val_loss did not improve from 0.02975
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0543 - val_loss: 0.0298 - val_mse: 0.0298 - val_mae: 0.1427 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 225/500

Epoch 00225: val_loss did not improve from 0.02975
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0534 - val_loss: 0.0298 - val_mse: 0.0298 - val_mae: 0.1426 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 226/500

Epoch 00226: val_loss improved from 0.02975 to 0.02947, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0557 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1417 - lr: 1.0000e-05 - 138ms/epoch - 9ms/step
Epoch 227/500

Epoch 00227: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0554 - val_loss: 0.0297 - val_mse: 0.0297 - val_mae: 0.1423 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 228/500

Epoch 00228: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0297 - val_mse: 0.0297 - val_mae: 0.1424 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 229/500

Epoch 00229: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0547 - val_loss: 0.0300 - val_mse: 0.0300 - val_mae: 0.1434 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 230/500

Epoch 00230: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0566 - val_loss: 0.0299 - val_mse: 0.0299 - val_mae: 0.1433 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 231/500

Epoch 00231: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0565 - val_loss: 0.0298 - val_mse: 0.0298 - val_mae: 0.1427 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 232/500

Epoch 00232: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0560 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1424 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 233/500

Epoch 00233: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0581 - val_loss: 0.0299 - val_mse: 0.0299 - val_mae: 0.1434 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 234/500

Epoch 00234: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0530 - val_loss: 0.0301 - val_mse: 0.0301 - val_mae: 0.1439 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 235/500

Epoch 00235: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0578 - val_loss: 0.0299 - val_mse: 0.0299 - val_mae: 0.1433 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 236/500

Epoch 00236: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0546 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1421 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 237/500

Epoch 00237: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0544 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1423 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 238/500

Epoch 00238: val_loss did not improve from 0.02947
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1420 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 239/500

Epoch 00239: val_loss improved from 0.02947 to 0.02932, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0550 - val_loss: 0.0293 - val_mse: 0.0293 - val_mae: 0.1415 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 240/500

Epoch 00240: val_loss improved from 0.02932 to 0.02924, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0544 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1413 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 241/500

Epoch 00241: val_loss improved from 0.02924 to 0.02923, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0546 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1413 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 242/500

Epoch 00242: val_loss improved from 0.02923 to 0.02921, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0549 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1412 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 243/500

Epoch 00243: val_loss improved from 0.02921 to 0.02917, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0554 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1411 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 244/500

Epoch 00244: val_loss improved from 0.02917 to 0.02916, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0548 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1411 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 245/500

Epoch 00245: val_loss improved from 0.02916 to 0.02912, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0548 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1410 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 246/500

Epoch 00246: val_loss did not improve from 0.02912
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0551 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1411 - lr: 1.0000e-05 - 124ms/epoch - 8ms/step
Epoch 247/500

Epoch 00247: val_loss did not improve from 0.02912
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0546 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1411 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 248/500

Epoch 00248: val_loss improved from 0.02912 to 0.02882, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0552 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1402 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 249/500

Epoch 00249: val_loss did not improve from 0.02882
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0568 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1403 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 250/500

Epoch 00250: val_loss did not improve from 0.02882
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0544 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1405 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 251/500

Epoch 00251: val_loss did not improve from 0.02882
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0552 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1404 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 252/500

Epoch 00252: val_loss improved from 0.02882 to 0.02875, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0554 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1400 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 253/500

Epoch 00253: val_loss did not improve from 0.02875
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0539 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1404 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 254/500

Epoch 00254: val_loss did not improve from 0.02875
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0564 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1402 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 255/500

Epoch 00255: val_loss improved from 0.02875 to 0.02866, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0544 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1398 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 256/500

Epoch 00256: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0537 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1401 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 257/500

Epoch 00257: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0518 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1407 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 258/500

Epoch 00258: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0553 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1401 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 259/500

Epoch 00259: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0576 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1404 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 260/500

Epoch 00260: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0530 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1412 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 261/500

Epoch 00261: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0535 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1414 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 262/500

Epoch 00262: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0538 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1412 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 263/500

Epoch 00263: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0508 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1403 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 264/500

Epoch 00264: val_loss did not improve from 0.02866
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0566 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1403 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 265/500

Epoch 00265: val_loss improved from 0.02866 to 0.02860, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0519 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1398 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 266/500

Epoch 00266: val_loss improved from 0.02860 to 0.02857, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0528 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1398 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 267/500

Epoch 00267: val_loss improved from 0.02857 to 0.02838, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0546 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1392 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 268/500

Epoch 00268: val_loss improved from 0.02838 to 0.02837, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0548 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1392 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 269/500

Epoch 00269: val_loss improved from 0.02837 to 0.02814, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0513 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1385 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 270/500

Epoch 00270: val_loss improved from 0.02814 to 0.02807, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0550 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1382 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 271/500

Epoch 00271: val_loss improved from 0.02807 to 0.02798, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0535 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1380 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 272/500

Epoch 00272: val_loss improved from 0.02798 to 0.02796, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0505 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1379 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 273/500

Epoch 00273: val_loss did not improve from 0.02796
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0524 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1381 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 274/500

Epoch 00274: val_loss improved from 0.02796 to 0.02790, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0526 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1378 - lr: 1.0000e-05 - 135ms/epoch - 8ms/step
Epoch 275/500

Epoch 00275: val_loss improved from 0.02790 to 0.02780, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0502 - val_loss: 0.0278 - val_mse: 0.0278 - val_mae: 0.1375 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 276/500

Epoch 00276: val_loss improved from 0.02780 to 0.02779, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0560 - val_loss: 0.0278 - val_mse: 0.0278 - val_mae: 0.1375 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 277/500

Epoch 00277: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0535 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1379 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 278/500

Epoch 00278: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0534 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1384 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 279/500

Epoch 00279: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0514 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1387 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 280/500

Epoch 00280: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0511 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1390 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 281/500

Epoch 00281: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0506 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1384 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 282/500

Epoch 00282: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0529 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1383 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 283/500

Epoch 00283: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0529 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1383 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 284/500

Epoch 00284: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0558 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1384 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 285/500

Epoch 00285: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0535 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1383 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 286/500

Epoch 00286: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0533 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1384 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 287/500

Epoch 00287: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0521 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1384 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 288/500

Epoch 00288: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0532 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1386 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 289/500

Epoch 00289: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0554 - val_loss: 0.0278 - val_mse: 0.0278 - val_mae: 0.1378 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 290/500

Epoch 00290: val_loss did not improve from 0.02779
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0537 - val_loss: 0.0278 - val_mse: 0.0278 - val_mae: 0.1378 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 291/500

Epoch 00291: val_loss improved from 0.02779 to 0.02743, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0531 - val_loss: 0.0274 - val_mse: 0.0274 - val_mae: 0.1366 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 292/500

Epoch 00292: val_loss improved from 0.02743 to 0.02703, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0548 - val_loss: 0.0270 - val_mse: 0.0270 - val_mae: 0.1353 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 293/500

Epoch 00293: val_loss improved from 0.02703 to 0.02689, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0527 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1349 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 294/500

Epoch 00294: val_loss improved from 0.02689 to 0.02665, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0519 - val_loss: 0.0267 - val_mse: 0.0267 - val_mae: 0.1341 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 295/500

Epoch 00295: val_loss did not improve from 0.02665
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.0267 - val_mse: 0.0267 - val_mae: 0.1344 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 296/500

Epoch 00296: val_loss improved from 0.02665 to 0.02661, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0554 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1340 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 297/500

Epoch 00297: val_loss did not improve from 0.02661
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0541 - val_loss: 0.0267 - val_mse: 0.0267 - val_mae: 0.1343 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 298/500

Epoch 00298: val_loss did not improve from 0.02661
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0527 - val_loss: 0.0268 - val_mse: 0.0268 - val_mae: 0.1349 - lr: 1.0000e-05 - 125ms/epoch - 8ms/step
Epoch 299/500

Epoch 00299: val_loss improved from 0.02661 to 0.02654, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0541 - val_loss: 0.0265 - val_mse: 0.0265 - val_mae: 0.1339 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 300/500

Epoch 00300: val_loss improved from 0.02654 to 0.02646, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0533 - val_loss: 0.0265 - val_mse: 0.0265 - val_mae: 0.1337 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 301/500

Epoch 00301: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0524 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1342 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 302/500

Epoch 00302: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0556 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1350 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 303/500

Epoch 00303: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0493 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1357 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 304/500

Epoch 00304: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0551 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1357 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 305/500

Epoch 00305: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0506 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1360 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 306/500

Epoch 00306: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0518 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1359 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 307/500

Epoch 00307: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0531 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1352 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 308/500

Epoch 00308: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0494 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1360 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 309/500

Epoch 00309: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0516 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1359 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 310/500

Epoch 00310: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0544 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1352 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 311/500

Epoch 00311: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0541 - val_loss: 0.0267 - val_mse: 0.0267 - val_mae: 0.1348 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 312/500

Epoch 00312: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0514 - val_loss: 0.0270 - val_mse: 0.0270 - val_mae: 0.1355 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 313/500

Epoch 00313: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0530 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1365 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 314/500

Epoch 00314: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0502 - val_loss: 0.0273 - val_mse: 0.0273 - val_mae: 0.1368 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 315/500

Epoch 00315: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0508 - val_loss: 0.0276 - val_mse: 0.0276 - val_mae: 0.1375 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 316/500

Epoch 00316: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0549 - val_loss: 0.0274 - val_mse: 0.0274 - val_mae: 0.1372 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 317/500

Epoch 00317: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1365 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 318/500

Epoch 00318: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0525 - val_loss: 0.0270 - val_mse: 0.0270 - val_mae: 0.1359 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 319/500

Epoch 00319: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0509 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1344 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 320/500

Epoch 00320: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0541 - val_loss: 0.0267 - val_mse: 0.0267 - val_mae: 0.1348 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 321/500

Epoch 00321: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0521 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1356 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 322/500

Epoch 00322: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0558 - val_loss: 0.0268 - val_mse: 0.0268 - val_mae: 0.1352 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 323/500

Epoch 00323: val_loss did not improve from 0.02646
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0534 - val_loss: 0.0267 - val_mse: 0.0267 - val_mae: 0.1348 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 324/500

Epoch 00324: val_loss improved from 0.02646 to 0.02646, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0538 - val_loss: 0.0265 - val_mse: 0.0265 - val_mae: 0.1341 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 325/500

Epoch 00325: val_loss improved from 0.02646 to 0.02624, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0514 - val_loss: 0.0262 - val_mse: 0.0262 - val_mae: 0.1335 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 326/500

Epoch 00326: val_loss did not improve from 0.02624
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0549 - val_loss: 0.0264 - val_mse: 0.0264 - val_mae: 0.1339 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 327/500

Epoch 00327: val_loss did not improve from 0.02624
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0525 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1347 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 328/500

Epoch 00328: val_loss did not improve from 0.02624
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0513 - val_loss: 0.0264 - val_mse: 0.0264 - val_mae: 0.1341 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 329/500

Epoch 00329: val_loss improved from 0.02624 to 0.02613, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0509 - val_loss: 0.0261 - val_mse: 0.0261 - val_mae: 0.1331 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 330/500

Epoch 00330: val_loss improved from 0.02613 to 0.02586, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.0259 - val_mse: 0.0259 - val_mae: 0.1323 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 331/500

Epoch 00331: val_loss improved from 0.02586 to 0.02575, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0471 - val_loss: 0.0257 - val_mse: 0.0257 - val_mae: 0.1319 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 332/500

Epoch 00332: val_loss improved from 0.02575 to 0.02561, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0503 - val_loss: 0.0256 - val_mse: 0.0256 - val_mae: 0.1315 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 333/500

Epoch 00333: val_loss improved from 0.02561 to 0.02540, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0516 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1308 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 334/500

Epoch 00334: val_loss improved from 0.02540 to 0.02523, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0537 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1303 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 335/500

Epoch 00335: val_loss did not improve from 0.02523
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0539 - val_loss: 0.0253 - val_mse: 0.0253 - val_mae: 0.1304 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 336/500

Epoch 00336: val_loss improved from 0.02523 to 0.02516, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0530 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1300 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 337/500

Epoch 00337: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0482 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1302 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 338/500

Epoch 00338: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0520 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1308 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 339/500

Epoch 00339: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0513 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1309 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 340/500

Epoch 00340: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0544 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1308 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 341/500

Epoch 00341: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0512 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1308 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 342/500

Epoch 00342: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0515 - val_loss: 0.0255 - val_mse: 0.0255 - val_mae: 0.1313 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 343/500

Epoch 00343: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0503 - val_loss: 0.0256 - val_mse: 0.0256 - val_mae: 0.1315 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 344/500

Epoch 00344: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0522 - val_loss: 0.0257 - val_mse: 0.0257 - val_mae: 0.1319 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 345/500

Epoch 00345: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0257 - val_mse: 0.0257 - val_mae: 0.1319 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 346/500

Epoch 00346: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0506 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1309 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 347/500

Epoch 00347: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0533 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1310 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 348/500

Epoch 00348: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0507 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1304 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 349/500

Epoch 00349: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0546 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1302 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 350/500

Epoch 00350: val_loss did not improve from 0.02516
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0488 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1305 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 351/500

Epoch 00351: val_loss improved from 0.02516 to 0.02512, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0520 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1301 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 352/500

Epoch 00352: val_loss improved from 0.02512 to 0.02486, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0529 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1293 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 353/500

Epoch 00353: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0509 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1296 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 354/500

Epoch 00354: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0528 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1301 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 355/500

Epoch 00355: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0500 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1310 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 356/500

Epoch 00356: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0529 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1312 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 357/500

Epoch 00357: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0521 - val_loss: 0.0253 - val_mse: 0.0253 - val_mae: 0.1309 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 358/500

Epoch 00358: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0521 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1304 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 359/500

Epoch 00359: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0494 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1302 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 360/500

Epoch 00360: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1310 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 361/500

Epoch 00361: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0502 - val_loss: 0.0254 - val_mse: 0.0254 - val_mae: 0.1313 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 362/500

Epoch 00362: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1307 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 363/500

Epoch 00363: val_loss did not improve from 0.02486
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1306 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 364/500

Epoch 00364: val_loss improved from 0.02486 to 0.02479, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0248 - val_mse: 0.0248 - val_mae: 0.1292 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 365/500

Epoch 00365: val_loss improved from 0.02479 to 0.02463, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0497 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1287 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 366/500

Epoch 00366: val_loss did not improve from 0.02463
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0527 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1297 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 367/500

Epoch 00367: val_loss did not improve from 0.02463
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0506 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1297 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 368/500

Epoch 00368: val_loss did not improve from 0.02463
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0522 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1291 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 369/500

Epoch 00369: val_loss did not improve from 0.02463
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0518 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1289 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 370/500

Epoch 00370: val_loss did not improve from 0.02463
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0536 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1289 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 371/500

Epoch 00371: val_loss improved from 0.02463 to 0.02450, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0509 - val_loss: 0.0245 - val_mse: 0.0245 - val_mae: 0.1284 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 372/500

Epoch 00372: val_loss improved from 0.02450 to 0.02412, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0507 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1271 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 373/500

Epoch 00373: val_loss improved from 0.02412 to 0.02408, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0503 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1270 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 374/500

Epoch 00374: val_loss did not improve from 0.02408
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1272 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 375/500

Epoch 00375: val_loss did not improve from 0.02408
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0528 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1274 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 376/500

Epoch 00376: val_loss improved from 0.02408 to 0.02405, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0504 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1270 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 377/500

Epoch 00377: val_loss did not improve from 0.02405
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0515 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1271 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 378/500

Epoch 00378: val_loss did not improve from 0.02405
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0511 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1270 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 379/500

Epoch 00379: val_loss improved from 0.02405 to 0.02386, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0502 - val_loss: 0.0239 - val_mse: 0.0239 - val_mae: 0.1263 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 380/500

Epoch 00380: val_loss improved from 0.02386 to 0.02352, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0500 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1252 - lr: 1.0000e-05 - 122ms/epoch - 8ms/step
Epoch 381/500

Epoch 00381: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0530 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1259 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 382/500

Epoch 00382: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0528 - val_loss: 0.0239 - val_mse: 0.0239 - val_mae: 0.1265 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 383/500

Epoch 00383: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0499 - val_loss: 0.0240 - val_mse: 0.0240 - val_mae: 0.1271 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 384/500

Epoch 00384: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0511 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1274 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 385/500

Epoch 00385: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0516 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1272 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 386/500

Epoch 00386: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0509 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1275 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 387/500

Epoch 00387: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0486 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1260 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 388/500

Epoch 00388: val_loss did not improve from 0.02352
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0497 - val_loss: 0.0236 - val_mse: 0.0236 - val_mae: 0.1256 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 389/500

Epoch 00389: val_loss improved from 0.02352 to 0.02352, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0494 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1254 - lr: 1.0000e-05 - 121ms/epoch - 8ms/step
Epoch 390/500

Epoch 00390: val_loss improved from 0.02352 to 0.02337, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1249 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 391/500

Epoch 00391: val_loss improved from 0.02337 to 0.02320, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0511 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1243 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 392/500

Epoch 00392: val_loss did not improve from 0.02320
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0500 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1246 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 393/500

Epoch 00393: val_loss improved from 0.02320 to 0.02318, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0513 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1243 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 394/500

Epoch 00394: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1244 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 395/500

Epoch 00395: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0505 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1244 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 396/500

Epoch 00396: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0513 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1246 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 397/500

Epoch 00397: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0520 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1255 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 398/500

Epoch 00398: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0515 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1256 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 399/500

Epoch 00399: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1260 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 400/500

Epoch 00400: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0519 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1263 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 401/500

Epoch 00401: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0493 - val_loss: 0.0236 - val_mse: 0.0236 - val_mae: 0.1259 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 402/500

Epoch 00402: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0507 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1253 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 403/500

Epoch 00403: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0511 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1248 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 404/500

Epoch 00404: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1263 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 405/500

Epoch 00405: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0493 - val_loss: 0.0239 - val_mse: 0.0239 - val_mae: 0.1269 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 406/500

Epoch 00406: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0514 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1262 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 407/500

Epoch 00407: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0236 - val_mse: 0.0236 - val_mae: 0.1260 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 408/500

Epoch 00408: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0496 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1255 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 409/500

Epoch 00409: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0497 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1263 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 410/500

Epoch 00410: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0530 - val_loss: 0.0238 - val_mse: 0.0238 - val_mae: 0.1267 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 411/500

Epoch 00411: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0510 - val_loss: 0.0239 - val_mse: 0.0239 - val_mae: 0.1269 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 412/500

Epoch 00412: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0511 - val_loss: 0.0236 - val_mse: 0.0236 - val_mae: 0.1258 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 413/500

Epoch 00413: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0501 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1254 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 414/500

Epoch 00414: val_loss did not improve from 0.02318
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0487 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1250 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 415/500

Epoch 00415: val_loss improved from 0.02318 to 0.02312, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0505 - val_loss: 0.0231 - val_mse: 0.0231 - val_mae: 0.1244 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 416/500

Epoch 00416: val_loss improved from 0.02312 to 0.02293, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0229 - val_mse: 0.0229 - val_mae: 0.1238 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 417/500

Epoch 00417: val_loss improved from 0.02293 to 0.02284, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0512 - val_loss: 0.0228 - val_mse: 0.0228 - val_mae: 0.1235 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 418/500

Epoch 00418: val_loss did not improve from 0.02284
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0510 - val_loss: 0.0230 - val_mse: 0.0230 - val_mae: 0.1241 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 419/500

Epoch 00419: val_loss did not improve from 0.02284
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0503 - val_loss: 0.0231 - val_mse: 0.0231 - val_mae: 0.1245 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 420/500

Epoch 00420: val_loss did not improve from 0.02284
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0500 - val_loss: 0.0229 - val_mse: 0.0229 - val_mae: 0.1239 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 421/500

Epoch 00421: val_loss improved from 0.02284 to 0.02278, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0482 - val_loss: 0.0228 - val_mse: 0.0228 - val_mae: 0.1234 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 422/500

Epoch 00422: val_loss improved from 0.02278 to 0.02269, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1231 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 423/500

Epoch 00423: val_loss did not improve from 0.02269
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0490 - val_loss: 0.0228 - val_mse: 0.0228 - val_mae: 0.1235 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 424/500

Epoch 00424: val_loss improved from 0.02269 to 0.02268, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0491 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1231 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 425/500

Epoch 00425: val_loss improved from 0.02268 to 0.02262, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0497 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1228 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 426/500

Epoch 00426: val_loss did not improve from 0.02262
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0526 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1229 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 427/500

Epoch 00427: val_loss did not improve from 0.02262
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0503 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1231 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 428/500

Epoch 00428: val_loss improved from 0.02262 to 0.02261, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0498 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1229 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 429/500

Epoch 00429: val_loss improved from 0.02261 to 0.02252, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0489 - val_loss: 0.0225 - val_mse: 0.0225 - val_mae: 0.1226 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 430/500

Epoch 00430: val_loss did not improve from 0.02252
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0506 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1231 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 431/500

Epoch 00431: val_loss did not improve from 0.02252
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0501 - val_loss: 0.0229 - val_mse: 0.0229 - val_mae: 0.1240 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 432/500

Epoch 00432: val_loss did not improve from 0.02252
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0491 - val_loss: 0.0230 - val_mse: 0.0230 - val_mae: 0.1241 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 433/500

Epoch 00433: val_loss did not improve from 0.02252
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0505 - val_loss: 0.0228 - val_mse: 0.0228 - val_mae: 0.1237 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 434/500

Epoch 00434: val_loss did not improve from 0.02252
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0501 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1231 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 435/500

Epoch 00435: val_loss improved from 0.02252 to 0.02229, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0496 - val_loss: 0.0223 - val_mse: 0.0223 - val_mae: 0.1219 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 436/500

Epoch 00436: val_loss improved from 0.02229 to 0.02221, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0516 - val_loss: 0.0222 - val_mse: 0.0222 - val_mae: 0.1216 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 437/500

Epoch 00437: val_loss did not improve from 0.02221
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0225 - val_mse: 0.0225 - val_mae: 0.1228 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 438/500

Epoch 00438: val_loss did not improve from 0.02221
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0493 - val_loss: 0.0225 - val_mse: 0.0225 - val_mae: 0.1225 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 439/500

Epoch 00439: val_loss did not improve from 0.02221
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0518 - val_loss: 0.0223 - val_mse: 0.0223 - val_mae: 0.1219 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 440/500

Epoch 00440: val_loss improved from 0.02221 to 0.02216, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0480 - val_loss: 0.0222 - val_mse: 0.0222 - val_mae: 0.1215 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 441/500

Epoch 00441: val_loss improved from 0.02216 to 0.02199, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0475 - val_loss: 0.0220 - val_mse: 0.0220 - val_mae: 0.1209 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 442/500

Epoch 00442: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0511 - val_loss: 0.0221 - val_mse: 0.0221 - val_mae: 0.1213 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 443/500

Epoch 00443: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0520 - val_loss: 0.0223 - val_mse: 0.0223 - val_mae: 0.1222 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 444/500

Epoch 00444: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0485 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1234 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 445/500

Epoch 00445: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0489 - val_loss: 0.0229 - val_mse: 0.0229 - val_mae: 0.1242 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 446/500

Epoch 00446: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0489 - val_loss: 0.0229 - val_mse: 0.0229 - val_mae: 0.1242 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 447/500

Epoch 00447: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0473 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1235 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 448/500

Epoch 00448: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0503 - val_loss: 0.0225 - val_mse: 0.0225 - val_mae: 0.1228 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 449/500

Epoch 00449: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0505 - val_loss: 0.0225 - val_mse: 0.0225 - val_mae: 0.1227 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 450/500

Epoch 00450: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0525 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1237 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 451/500

Epoch 00451: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0511 - val_loss: 0.0227 - val_mse: 0.0227 - val_mae: 0.1237 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 452/500

Epoch 00452: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0488 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1233 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 453/500

Epoch 00453: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0478 - val_loss: 0.0224 - val_mse: 0.0224 - val_mae: 0.1226 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 454/500

Epoch 00454: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0496 - val_loss: 0.0223 - val_mse: 0.0223 - val_mae: 0.1221 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 455/500

Epoch 00455: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0500 - val_loss: 0.0222 - val_mse: 0.0222 - val_mae: 0.1219 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 456/500

Epoch 00456: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0486 - val_loss: 0.0223 - val_mse: 0.0223 - val_mae: 0.1222 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 457/500

Epoch 00457: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0222 - val_mse: 0.0222 - val_mae: 0.1221 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 458/500

Epoch 00458: val_loss did not improve from 0.02199
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0511 - val_loss: 0.0220 - val_mse: 0.0220 - val_mae: 0.1213 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 459/500

Epoch 00459: val_loss improved from 0.02199 to 0.02181, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0485 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1206 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 460/500

Epoch 00460: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0492 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1207 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 461/500

Epoch 00461: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0475 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1209 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 462/500

Epoch 00462: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0512 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1209 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 463/500

Epoch 00463: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0505 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1210 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 464/500

Epoch 00464: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0490 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1209 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 465/500

Epoch 00465: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0495 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1209 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 466/500

Epoch 00466: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0505 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1211 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 467/500

Epoch 00467: val_loss did not improve from 0.02181
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0510 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1209 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 468/500

Epoch 00468: val_loss improved from 0.02181 to 0.02172, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0492 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1203 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 469/500

Epoch 00469: val_loss did not improve from 0.02172
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0487 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1204 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 470/500

Epoch 00470: val_loss improved from 0.02172 to 0.02164, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0486 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1201 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 471/500

Epoch 00471: val_loss improved from 0.02164 to 0.02155, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0488 - val_loss: 0.0215 - val_mse: 0.0215 - val_mae: 0.1198 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 472/500

Epoch 00472: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0482 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1202 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 473/500

Epoch 00473: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0480 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1209 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 474/500

Epoch 00474: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0495 - val_loss: 0.0220 - val_mse: 0.0220 - val_mae: 0.1215 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 475/500

Epoch 00475: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0516 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1210 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 476/500

Epoch 00476: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0506 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1201 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 477/500

Epoch 00477: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0495 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1203 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 478/500

Epoch 00478: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0481 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1207 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 479/500

Epoch 00479: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0471 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1209 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 480/500

Epoch 00480: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0487 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1206 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 481/500

Epoch 00481: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0515 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1203 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 482/500

Epoch 00482: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0502 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1205 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 483/500

Epoch 00483: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0485 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1208 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 484/500

Epoch 00484: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0495 - val_loss: 0.0219 - val_mse: 0.0219 - val_mae: 0.1213 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 485/500

Epoch 00485: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0460 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1210 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 486/500

Epoch 00486: val_loss did not improve from 0.02155
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0493 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1206 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 487/500

Epoch 00487: val_loss improved from 0.02155 to 0.02147, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0501 - val_loss: 0.0215 - val_mse: 0.0215 - val_mae: 0.1197 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 488/500

Epoch 00488: val_loss improved from 0.02147 to 0.02127, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0503 - val_loss: 0.0213 - val_mse: 0.0213 - val_mae: 0.1190 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 489/500

Epoch 00489: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0481 - val_loss: 0.0213 - val_mse: 0.0213 - val_mae: 0.1191 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 490/500

Epoch 00490: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0482 - val_loss: 0.0214 - val_mse: 0.0214 - val_mae: 0.1197 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 491/500

Epoch 00491: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0475 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1202 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 492/500

Epoch 00492: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0476 - val_loss: 0.0214 - val_mse: 0.0214 - val_mae: 0.1197 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 493/500

Epoch 00493: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0506 - val_loss: 0.0214 - val_mse: 0.0214 - val_mae: 0.1196 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 494/500

Epoch 00494: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0516 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1202 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 495/500

Epoch 00495: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0484 - val_loss: 0.0217 - val_mse: 0.0217 - val_mae: 0.1207 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 496/500

Epoch 00496: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0484 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1205 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 497/500

Epoch 00497: val_loss did not improve from 0.02127
16/16 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0496 - val_loss: 0.0214 - val_mse: 0.0214 - val_mae: 0.1198 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 498/500

Epoch 00498: val_loss improved from 0.02127 to 0.02122, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0491 - val_loss: 0.0212 - val_mse: 0.0212 - val_mae: 0.1190 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 499/500

Epoch 00499: val_loss did not improve from 0.02122
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0516 - val_loss: 0.0213 - val_mse: 0.0213 - val_mae: 0.1193 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 500/500

Epoch 00500: val_loss improved from 0.02122 to 0.02099, saving model to LSTM3.h5
16/16 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0496 - val_loss: 0.0210 - val_mse: 0.0210 - val_mae: 0.1183 - lr: 1.0000e-05 - 122ms/epoch - 8ms/step
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.40 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4264.089, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3793.930, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.26 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3564.923, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3427.258, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.24 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.45 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3429.258, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.750 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1709.629
Date:                Sun, 12 Dec 2021   AIC                           3427.258
Time:                        15:01:30   BIC                           3446.021
Sample:                             0   HQIC                          3434.464
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1981      0.003   -389.386      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.699      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.737      0.000      -0.410      -0.387
sigma2         4.0860      0.019    215.311      0.000       4.049       4.123
===================================================================================
Ljung-Box (L1) (Q):                  14.57   Jarque-Bera (JB):           2460901.70
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.02131, saving model to LSTM3.h5
17/17 - 2s - loss: 0.8890 - mse: 0.8890 - mae: 0.7526 - val_loss: 0.0213 - val_mse: 0.0213 - val_mae: 0.1190 - lr: 0.0010 - 2s/epoch - 127ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.02131 to 0.02058, saving model to LSTM3.h5
17/17 - 0s - loss: 0.1482 - mse: 0.1482 - mae: 0.3315 - val_loss: 0.0206 - val_mse: 0.0206 - val_mae: 0.1164 - lr: 0.0010 - 99ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0742 - mse: 0.0742 - mae: 0.2245 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1535 - lr: 0.0010 - 90ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0478 - mse: 0.0478 - mae: 0.1730 - val_loss: 0.0454 - val_mse: 0.0454 - val_mae: 0.1777 - lr: 0.0010 - 90ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0391 - mse: 0.0391 - mae: 0.1575 - val_loss: 0.0570 - val_mse: 0.0570 - val_mae: 0.2042 - lr: 0.0010 - 87ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0314 - mse: 0.0314 - mae: 0.1422 - val_loss: 0.0658 - val_mse: 0.0658 - val_mae: 0.2229 - lr: 0.0010 - 84ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0287 - mse: 0.0287 - mae: 0.1361 - val_loss: 0.0708 - val_mse: 0.0708 - val_mae: 0.2332 - lr: 0.0010 - 88ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0265 - mse: 0.0265 - mae: 0.1282 - val_loss: 0.0709 - val_mse: 0.0709 - val_mae: 0.2336 - lr: 1.0000e-04 - 107ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0251 - mse: 0.0251 - mae: 0.1272 - val_loss: 0.0713 - val_mse: 0.0713 - val_mae: 0.2345 - lr: 1.0000e-04 - 117ms/epoch - 7ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0249 - mse: 0.0249 - mae: 0.1266 - val_loss: 0.0718 - val_mse: 0.0718 - val_mae: 0.2355 - lr: 1.0000e-04 - 111ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0238 - mse: 0.0238 - mae: 0.1242 - val_loss: 0.0720 - val_mse: 0.0720 - val_mae: 0.2359 - lr: 1.0000e-04 - 103ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00012: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0255 - mse: 0.0255 - mae: 0.1280 - val_loss: 0.0724 - val_mse: 0.0724 - val_mae: 0.2369 - lr: 1.0000e-04 - 106ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0245 - mse: 0.0245 - mae: 0.1244 - val_loss: 0.0725 - val_mse: 0.0725 - val_mae: 0.2370 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0245 - mse: 0.0245 - mae: 0.1249 - val_loss: 0.0725 - val_mse: 0.0725 - val_mae: 0.2370 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0255 - mse: 0.0255 - mae: 0.1276 - val_loss: 0.0725 - val_mse: 0.0725 - val_mae: 0.2371 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0246 - mse: 0.0246 - mae: 0.1245 - val_loss: 0.0725 - val_mse: 0.0725 - val_mae: 0.2371 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00017: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0242 - mse: 0.0242 - mae: 0.1238 - val_loss: 0.0726 - val_mse: 0.0726 - val_mae: 0.2372 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0242 - mse: 0.0242 - mae: 0.1228 - val_loss: 0.0726 - val_mse: 0.0726 - val_mae: 0.2372 - lr: 1.0000e-05 - 92ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0241 - mse: 0.0241 - mae: 0.1229 - val_loss: 0.0726 - val_mse: 0.0726 - val_mae: 0.2372 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0248 - mse: 0.0248 - mae: 0.1262 - val_loss: 0.0726 - val_mse: 0.0726 - val_mae: 0.2374 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0248 - mse: 0.0248 - mae: 0.1246 - val_loss: 0.0727 - val_mse: 0.0727 - val_mae: 0.2375 - lr: 1.0000e-05 - 92ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0255 - mse: 0.0255 - mae: 0.1286 - val_loss: 0.0727 - val_mse: 0.0727 - val_mae: 0.2376 - lr: 1.0000e-05 - 89ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0242 - mse: 0.0242 - mae: 0.1249 - val_loss: 0.0728 - val_mse: 0.0728 - val_mae: 0.2376 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0249 - mse: 0.0249 - mae: 0.1279 - val_loss: 0.0728 - val_mse: 0.0728 - val_mae: 0.2377 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0242 - mse: 0.0242 - mae: 0.1231 - val_loss: 0.0729 - val_mse: 0.0729 - val_mae: 0.2378 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0234 - mse: 0.0234 - mae: 0.1214 - val_loss: 0.0729 - val_mse: 0.0729 - val_mae: 0.2380 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0228 - mse: 0.0228 - mae: 0.1210 - val_loss: 0.0730 - val_mse: 0.0730 - val_mae: 0.2382 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0221 - mse: 0.0221 - mae: 0.1218 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2383 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0245 - mse: 0.0245 - mae: 0.1267 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2383 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0262 - mse: 0.0262 - mae: 0.1286 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2384 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0245 - mse: 0.0245 - mae: 0.1259 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2384 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0248 - mse: 0.0248 - mae: 0.1273 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2383 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0249 - mse: 0.0249 - mae: 0.1275 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2384 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0223 - mse: 0.0223 - mae: 0.1180 - val_loss: 0.0731 - val_mse: 0.0731 - val_mae: 0.2385 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0229 - mse: 0.0229 - mae: 0.1227 - val_loss: 0.0732 - val_mse: 0.0732 - val_mae: 0.2387 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0252 - mse: 0.0252 - mae: 0.1272 - val_loss: 0.0733 - val_mse: 0.0733 - val_mae: 0.2388 - lr: 1.0000e-05 - 93ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0254 - mse: 0.0254 - mae: 0.1276 - val_loss: 0.0733 - val_mse: 0.0733 - val_mae: 0.2389 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0230 - mse: 0.0230 - mae: 0.1210 - val_loss: 0.0735 - val_mse: 0.0735 - val_mae: 0.2392 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0245 - mse: 0.0245 - mae: 0.1249 - val_loss: 0.0735 - val_mse: 0.0735 - val_mae: 0.2392 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0219 - mse: 0.0219 - mae: 0.1178 - val_loss: 0.0735 - val_mse: 0.0735 - val_mae: 0.2394 - lr: 1.0000e-05 - 90ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0240 - mse: 0.0240 - mae: 0.1242 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2395 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0239 - mse: 0.0239 - mae: 0.1250 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2396 - lr: 1.0000e-05 - 90ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0243 - mse: 0.0243 - mae: 0.1244 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2395 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0228 - mse: 0.0228 - mae: 0.1221 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2395 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0219 - mse: 0.0219 - mae: 0.1177 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2396 - lr: 1.0000e-05 - 92ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0217 - mse: 0.0217 - mae: 0.1162 - val_loss: 0.0737 - val_mse: 0.0737 - val_mae: 0.2397 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0265 - mse: 0.0265 - mae: 0.1279 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2396 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0236 - mse: 0.0236 - mae: 0.1218 - val_loss: 0.0736 - val_mse: 0.0736 - val_mae: 0.2396 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0226 - mse: 0.0226 - mae: 0.1212 - val_loss: 0.0737 - val_mse: 0.0737 - val_mae: 0.2397 - lr: 1.0000e-05 - 90ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0227 - mse: 0.0227 - mae: 0.1186 - val_loss: 0.0737 - val_mse: 0.0737 - val_mae: 0.2397 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0237 - mse: 0.0237 - mae: 0.1222 - val_loss: 0.0737 - val_mse: 0.0737 - val_mae: 0.2398 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02058
17/17 - 0s - loss: 0.0208 - mse: 0.0208 - mae: 0.1157 - val_loss: 0.0737 - val_mse: 0.0737 - val_mae: 0.2399 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 00052: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658

WMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 24.586224407987817 
RMSE:	 4.958449798877449 
MAPE:	 3.970226889097132
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.42 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4436.126, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3965.317, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.38 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3736.589, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3598.951, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.91 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.90 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3600.951, Time=0.21 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.040 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1795.475
Date:                Sun, 12 Dec 2021   AIC                           3598.951
Time:                        15:02:45   BIC                           3617.714
Sample:                             0   HQIC                          3606.157
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1983      0.003   -389.581      0.000      -1.204      -1.192
ar.L2         -0.8973      0.006   -139.732      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.649      0.000      -0.410      -0.387
sigma2         5.0573      0.023    215.292      0.000       5.011       5.103
===================================================================================
Ljung-Box (L1) (Q):                  14.41   Jarque-Bera (JB):           2460553.80
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.89
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.74
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.14531, saving model to LSTM3.h5
10/10 - 3s - loss: 0.4588 - mse: 0.4588 - mae: 0.5822 - val_loss: 0.1453 - val_mse: 0.1453 - val_mae: 0.3471 - lr: 0.0010 - 3s/epoch - 252ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.14531 to 0.06661, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0766 - mse: 0.0766 - mae: 0.2246 - val_loss: 0.0666 - val_mse: 0.0666 - val_mae: 0.2219 - lr: 0.0010 - 86ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.06661 to 0.03347, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0677 - mse: 0.0677 - mae: 0.2234 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1513 - lr: 0.0010 - 90ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.03347 to 0.01798, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0283 - mse: 0.0283 - mae: 0.1346 - val_loss: 0.0180 - val_mse: 0.0180 - val_mae: 0.1095 - lr: 0.0010 - 76ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.01798 to 0.01389, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0252 - mse: 0.0252 - mae: 0.1227 - val_loss: 0.0139 - val_mse: 0.0139 - val_mae: 0.0965 - lr: 0.0010 - 77ms/epoch - 8ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.01389 to 0.01325, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0227 - mse: 0.0227 - mae: 0.1170 - val_loss: 0.0132 - val_mse: 0.0132 - val_mae: 0.0939 - lr: 0.0010 - 90ms/epoch - 9ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.01325
10/10 - 0s - loss: 0.0199 - mse: 0.0199 - mae: 0.1116 - val_loss: 0.0133 - val_mse: 0.0133 - val_mae: 0.0931 - lr: 0.0010 - 74ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.01325 to 0.01313, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0160 - mse: 0.0160 - mae: 0.1009 - val_loss: 0.0131 - val_mse: 0.0131 - val_mae: 0.0922 - lr: 0.0010 - 72ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.01313 to 0.01294, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0150 - mse: 0.0150 - mae: 0.0960 - val_loss: 0.0129 - val_mse: 0.0129 - val_mae: 0.0916 - lr: 0.0010 - 85ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.01294 to 0.01274, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0132 - mse: 0.0132 - mae: 0.0896 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0911 - lr: 0.0010 - 84ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.01274 to 0.01251, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0128 - mse: 0.0128 - mae: 0.0880 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0907 - lr: 0.0010 - 82ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.01251
10/10 - 0s - loss: 0.0113 - mse: 0.0113 - mae: 0.0834 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0921 - lr: 0.0010 - 82ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.01251 to 0.01239, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0112 - mse: 0.0112 - mae: 0.0834 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0910 - lr: 0.0010 - 86ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.01239 to 0.01209, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0103 - mse: 0.0103 - mae: 0.0797 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0898 - lr: 0.0010 - 86ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.01209
10/10 - 0s - loss: 0.0097 - mse: 0.0097 - mae: 0.0784 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0904 - lr: 0.0010 - 69ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.01209
10/10 - 0s - loss: 0.0093 - mse: 0.0093 - mae: 0.0765 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0905 - lr: 0.0010 - 58ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.01209 to 0.01191, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0091 - mse: 0.0091 - mae: 0.0755 - val_loss: 0.0119 - val_mse: 0.0119 - val_mae: 0.0895 - lr: 0.0010 - 96ms/epoch - 10ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.01191 to 0.01123, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0719 - val_loss: 0.0112 - val_mse: 0.0112 - val_mae: 0.0859 - lr: 0.0010 - 74ms/epoch - 7ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.01123 to 0.01102, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0685 - val_loss: 0.0110 - val_mse: 0.0110 - val_mae: 0.0853 - lr: 0.0010 - 76ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss improved from 0.01102 to 0.01081, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0697 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0845 - lr: 0.0010 - 76ms/epoch - 8ms/step
Epoch 21/500

Epoch 00021: val_loss improved from 0.01081 to 0.01046, saving model to LSTM3.h5
10/10 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0710 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0826 - lr: 0.0010 - 88ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0675 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0860 - lr: 0.0010 - 68ms/epoch - 7ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0681 - val_loss: 0.0114 - val_mse: 0.0114 - val_mae: 0.0880 - lr: 0.0010 - 64ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0666 - val_loss: 0.0131 - val_mse: 0.0131 - val_mae: 0.0949 - lr: 0.0010 - 58ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0656 - val_loss: 0.0126 - val_mse: 0.0126 - val_mae: 0.0934 - lr: 0.0010 - 73ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00026: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0623 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0930 - lr: 0.0010 - 61ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0628 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0925 - lr: 1.0000e-04 - 65ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0619 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-04 - 64ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0602 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0931 - lr: 1.0000e-04 - 70ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0609 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0932 - lr: 1.0000e-04 - 70ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00031: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0638 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0928 - lr: 1.0000e-04 - 73ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0611 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0618 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0928 - lr: 1.0000e-05 - 59ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0616 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0929 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0603 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0930 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00036: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0654 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0930 - lr: 1.0000e-05 - 66ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0669 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0930 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0633 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0931 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0644 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0931 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0620 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0930 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0644 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0929 - lr: 1.0000e-05 - 61ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0629 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0930 - lr: 1.0000e-05 - 62ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0598 - val_loss: 0.0125 - val_mse: 0.0125 - val_mae: 0.0929 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0631 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0928 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0625 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-05 - 60ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0637 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0606 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0608 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-05 - 63ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0621 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0927 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0614 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0925 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0621 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0924 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0618 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0923 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0576 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0923 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0603 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0923 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0623 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0922 - lr: 1.0000e-05 - 79ms/epoch - 8ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0652 - val_loss: 0.0122 - val_mse: 0.0122 - val_mae: 0.0921 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0620 - val_loss: 0.0122 - val_mse: 0.0122 - val_mae: 0.0921 - lr: 1.0000e-05 - 63ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0605 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0922 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0616 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0923 - lr: 1.0000e-05 - 61ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0647 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0922 - lr: 1.0000e-05 - 58ms/epoch - 6ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0600 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0922 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0646 - val_loss: 0.0122 - val_mse: 0.0122 - val_mae: 0.0921 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0624 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0921 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0625 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0922 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0606 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0923 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0623 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0924 - lr: 1.0000e-05 - 65ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0594 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0925 - lr: 1.0000e-05 - 58ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0612 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0925 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0608 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0926 - lr: 1.0000e-05 - 79ms/epoch - 8ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0601 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0926 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.01046
10/10 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0612 - val_loss: 0.0123 - val_mse: 0.0123 - val_mae: 0.0925 - lr: 1.0000e-05 - 79ms/epoch - 8ms/step
Epoch 00071: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658

WMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 24.586224407987817 
RMSE:	 4.958449798877449 
MAPE:	 3.970226889097132

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	51.49% Accuracy
MSE:	 207.2547601932076 
RMSE:	 14.3963453762824 
MAPE:	 12.894635987621164
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.36 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4190.464, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3724.371, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.28 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3494.154, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3357.435, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.18 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.70 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3359.435, Time=0.24 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.989 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1674.717
Date:                Sun, 12 Dec 2021   AIC                           3357.435
Time:                        15:04:02   BIC                           3376.198
Sample:                             0   HQIC                          3364.641
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1955      0.003   -381.246      0.000      -1.202      -1.189
ar.L2         -0.8964      0.007   -135.835      0.000      -0.909      -0.883
ar.L3         -0.3971      0.006    -67.229      0.000      -0.409      -0.385
sigma2         3.7466      0.018    211.623      0.000       3.712       3.781
===================================================================================
Ljung-Box (L1) (Q):                  14.20   Jarque-Bera (JB):           2338363.32
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             3.76
Prob(H) (two-sided):                  0.00   Kurtosis:                       266.93
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.52870, saving model to LSTM3.h5
45/45 - 2s - loss: 0.1434 - mse: 0.1434 - mae: 0.3013 - val_loss: 0.5287 - val_mse: 0.5287 - val_mae: 0.6882 - lr: 0.0010 - 2s/epoch - 49ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.52870 to 0.22252, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0645 - mse: 0.0645 - mae: 0.2074 - val_loss: 0.2225 - val_mse: 0.2225 - val_mae: 0.4296 - lr: 0.0010 - 216ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.22252 to 0.11434, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0430 - mse: 0.0430 - mae: 0.1667 - val_loss: 0.1143 - val_mse: 0.1143 - val_mae: 0.2943 - lr: 0.0010 - 240ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.11434 to 0.07710, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0251 - mse: 0.0251 - mae: 0.1281 - val_loss: 0.0771 - val_mse: 0.0771 - val_mae: 0.2329 - lr: 0.0010 - 220ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.07710 to 0.05932, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0175 - mse: 0.0175 - mae: 0.1066 - val_loss: 0.0593 - val_mse: 0.0593 - val_mae: 0.1992 - lr: 0.0010 - 237ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.05932 to 0.05014, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0134 - mse: 0.0134 - mae: 0.0935 - val_loss: 0.0501 - val_mse: 0.0501 - val_mae: 0.1808 - lr: 0.0010 - 227ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05014
45/45 - 0s - loss: 0.0114 - mse: 0.0114 - mae: 0.0837 - val_loss: 0.0542 - val_mse: 0.0542 - val_mae: 0.1922 - lr: 0.0010 - 251ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05014
45/45 - 0s - loss: 0.0105 - mse: 0.0105 - mae: 0.0823 - val_loss: 0.0536 - val_mse: 0.0536 - val_mae: 0.1921 - lr: 0.0010 - 189ms/epoch - 4ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.05014 to 0.04707, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0085 - mse: 0.0085 - mae: 0.0739 - val_loss: 0.0471 - val_mse: 0.0471 - val_mae: 0.1781 - lr: 0.0010 - 213ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04707
45/45 - 0s - loss: 0.0090 - mse: 0.0090 - mae: 0.0764 - val_loss: 0.0554 - val_mse: 0.0554 - val_mae: 0.1979 - lr: 0.0010 - 187ms/epoch - 4ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.04707 to 0.04541, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0707 - val_loss: 0.0454 - val_mse: 0.0454 - val_mae: 0.1760 - lr: 0.0010 - 255ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04541
45/45 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0721 - val_loss: 0.0535 - val_mse: 0.0535 - val_mae: 0.1954 - lr: 0.0010 - 220ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.04541 to 0.04341, saving model to LSTM3.h5
45/45 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0644 - val_loss: 0.0434 - val_mse: 0.0434 - val_mae: 0.1726 - lr: 0.0010 - 245ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0645 - val_loss: 0.0495 - val_mse: 0.0495 - val_mae: 0.1879 - lr: 0.0010 - 178ms/epoch - 4ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0594 - val_loss: 0.0508 - val_mse: 0.0508 - val_mae: 0.1916 - lr: 0.0010 - 241ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0615 - val_loss: 0.0451 - val_mse: 0.0451 - val_mae: 0.1783 - lr: 0.0010 - 243ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0546 - val_loss: 0.0724 - val_mse: 0.0724 - val_mae: 0.2376 - lr: 0.0010 - 264ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00018: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0561 - val_loss: 0.0595 - val_mse: 0.0595 - val_mae: 0.2114 - lr: 0.0010 - 229ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0500 - val_loss: 0.0594 - val_mse: 0.0594 - val_mae: 0.2112 - lr: 1.0000e-04 - 224ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0508 - val_loss: 0.0593 - val_mse: 0.0593 - val_mae: 0.2112 - lr: 1.0000e-04 - 232ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0512 - val_loss: 0.0597 - val_mse: 0.0597 - val_mae: 0.2120 - lr: 1.0000e-04 - 218ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0519 - val_loss: 0.0601 - val_mse: 0.0601 - val_mae: 0.2130 - lr: 1.0000e-04 - 265ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00023: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0519 - val_loss: 0.0597 - val_mse: 0.0597 - val_mae: 0.2124 - lr: 1.0000e-04 - 231ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0495 - val_loss: 0.0599 - val_mse: 0.0599 - val_mae: 0.2128 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0479 - val_loss: 0.0598 - val_mse: 0.0598 - val_mae: 0.2126 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0482 - val_loss: 0.0599 - val_mse: 0.0599 - val_mae: 0.2127 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0492 - val_loss: 0.0598 - val_mse: 0.0598 - val_mae: 0.2125 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 28/500

Epoch 00028: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00028: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0597 - val_mse: 0.0597 - val_mae: 0.2123 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0496 - val_loss: 0.0598 - val_mse: 0.0598 - val_mae: 0.2126 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0459 - val_loss: 0.0598 - val_mse: 0.0598 - val_mae: 0.2126 - lr: 1.0000e-05 - 188ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0500 - val_loss: 0.0598 - val_mse: 0.0598 - val_mae: 0.2125 - lr: 1.0000e-05 - 201ms/epoch - 4ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0504 - val_loss: 0.0598 - val_mse: 0.0598 - val_mae: 0.2126 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0500 - val_loss: 0.0599 - val_mse: 0.0599 - val_mae: 0.2128 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0493 - val_loss: 0.0597 - val_mse: 0.0597 - val_mae: 0.2124 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0487 - val_loss: 0.0596 - val_mse: 0.0596 - val_mae: 0.2121 - lr: 1.0000e-05 - 198ms/epoch - 4ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0496 - val_loss: 0.0594 - val_mse: 0.0594 - val_mae: 0.2118 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0490 - val_loss: 0.0595 - val_mse: 0.0595 - val_mae: 0.2120 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0485 - val_loss: 0.0593 - val_mse: 0.0593 - val_mae: 0.2115 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0482 - val_loss: 0.0592 - val_mse: 0.0592 - val_mae: 0.2113 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0479 - val_loss: 0.0591 - val_mse: 0.0591 - val_mae: 0.2110 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0487 - val_loss: 0.0589 - val_mse: 0.0589 - val_mae: 0.2107 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0473 - val_loss: 0.0589 - val_mse: 0.0589 - val_mae: 0.2106 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0516 - val_loss: 0.0588 - val_mse: 0.0588 - val_mae: 0.2105 - lr: 1.0000e-05 - 195ms/epoch - 4ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0502 - val_loss: 0.0585 - val_mse: 0.0585 - val_mae: 0.2097 - lr: 1.0000e-05 - 188ms/epoch - 4ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0490 - val_loss: 0.0584 - val_mse: 0.0584 - val_mae: 0.2096 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0506 - val_loss: 0.0582 - val_mse: 0.0582 - val_mae: 0.2091 - lr: 1.0000e-05 - 194ms/epoch - 4ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0481 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.2087 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0496 - val_loss: 0.0578 - val_mse: 0.0578 - val_mae: 0.2083 - lr: 1.0000e-05 - 200ms/epoch - 4ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0506 - val_loss: 0.0579 - val_mse: 0.0579 - val_mae: 0.2085 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0512 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.2087 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0486 - val_loss: 0.0576 - val_mse: 0.0576 - val_mae: 0.2079 - lr: 1.0000e-05 - 195ms/epoch - 4ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0489 - val_loss: 0.0577 - val_mse: 0.0577 - val_mae: 0.2080 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0499 - val_loss: 0.0576 - val_mse: 0.0576 - val_mae: 0.2079 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0487 - val_loss: 0.0576 - val_mse: 0.0576 - val_mae: 0.2079 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0485 - val_loss: 0.0578 - val_mse: 0.0578 - val_mae: 0.2082 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0505 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.2089 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0476 - val_loss: 0.0582 - val_mse: 0.0582 - val_mae: 0.2092 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0485 - val_loss: 0.0582 - val_mse: 0.0582 - val_mae: 0.2091 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0503 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.2088 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0468 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.2087 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0493 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.2087 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0488 - val_loss: 0.0581 - val_mse: 0.0581 - val_mae: 0.2089 - lr: 1.0000e-05 - 199ms/epoch - 4ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.04341
45/45 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0488 - val_loss: 0.0581 - val_mse: 0.0581 - val_mae: 0.2091 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 00063: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658

WMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 24.586224407987817 
RMSE:	 4.958449798877449 
MAPE:	 3.970226889097132

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	51.49% Accuracy
MSE:	 207.2547601932076 
RMSE:	 14.3963453762824 
MAPE:	 12.894635987621164

KAMA
Prediction vs Close:		50.75% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 23.743754657069395 
RMSE:	 4.872756371610364 
MAPE:	 3.7850733762502107
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.35 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4212.289, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3747.746, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.26 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3523.401, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3387.759, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.24 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.84 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3389.758, Time=0.23 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.154 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1689.879
Date:                Sun, 12 Dec 2021   AIC                           3387.759
Time:                        15:05:27   BIC                           3406.522
Sample:                             0   HQIC                          3394.964
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1878      0.003   -345.315      0.000      -1.195      -1.181
ar.L2         -0.8876      0.007   -121.809      0.000      -0.902      -0.873
ar.L3         -0.3957      0.007    -60.127      0.000      -0.409      -0.383
sigma2         3.8904      0.020    193.404      0.000       3.851       3.930
===================================================================================
Ljung-Box (L1) (Q):                  13.21   Jarque-Bera (JB):           1659080.01
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.08   Skew:                             3.28
Prob(H) (two-sided):                  0.00   Kurtosis:                       225.31
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.09371, saving model to LSTM3.h5
58/58 - 3s - loss: 0.1380 - mse: 0.1380 - mae: 0.2774 - val_loss: 0.0937 - val_mse: 0.0937 - val_mae: 0.2769 - lr: 0.0010 - 3s/epoch - 48ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.09371 to 0.04685, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0497 - mse: 0.0497 - mae: 0.1767 - val_loss: 0.0468 - val_mse: 0.0468 - val_mae: 0.1731 - lr: 0.0010 - 303ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.04685 to 0.03945, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0222 - mse: 0.0222 - mae: 0.1174 - val_loss: 0.0395 - val_mse: 0.0395 - val_mae: 0.1598 - lr: 0.0010 - 277ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.03945
58/58 - 0s - loss: 0.0142 - mse: 0.0142 - mae: 0.0941 - val_loss: 0.0437 - val_mse: 0.0437 - val_mae: 0.1682 - lr: 0.0010 - 269ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.03945
58/58 - 0s - loss: 0.0143 - mse: 0.0143 - mae: 0.0960 - val_loss: 0.0416 - val_mse: 0.0416 - val_mae: 0.1632 - lr: 0.0010 - 241ms/epoch - 4ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.03945
58/58 - 0s - loss: 0.0115 - mse: 0.0115 - mae: 0.0853 - val_loss: 0.0458 - val_mse: 0.0458 - val_mae: 0.1734 - lr: 0.0010 - 275ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.03945
58/58 - 0s - loss: 0.0107 - mse: 0.0107 - mae: 0.0824 - val_loss: 0.0433 - val_mse: 0.0433 - val_mae: 0.1664 - lr: 0.0010 - 253ms/epoch - 4ms/step
Epoch 8/500

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00008: val_loss did not improve from 0.03945
58/58 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0704 - val_loss: 0.0470 - val_mse: 0.0470 - val_mae: 0.1766 - lr: 0.0010 - 252ms/epoch - 4ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.03945 to 0.03681, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0105 - mse: 0.0105 - mae: 0.0829 - val_loss: 0.0368 - val_mse: 0.0368 - val_mae: 0.1519 - lr: 1.0000e-04 - 287ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.03681 to 0.03586, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0717 - val_loss: 0.0359 - val_mse: 0.0359 - val_mae: 0.1489 - lr: 1.0000e-04 - 332ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.03586 to 0.03285, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0682 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1408 - lr: 1.0000e-04 - 299ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.03285
58/58 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0693 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1419 - lr: 1.0000e-04 - 243ms/epoch - 4ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.03285 to 0.03201, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0667 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1381 - lr: 1.0000e-04 - 273ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.03201 to 0.03175, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0645 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1373 - lr: 1.0000e-04 - 254ms/epoch - 4ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0618 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1391 - lr: 1.0000e-04 - 262ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0624 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1373 - lr: 1.0000e-04 - 245ms/epoch - 4ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0587 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1394 - lr: 1.0000e-04 - 296ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0608 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1385 - lr: 1.0000e-04 - 292ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00019: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0595 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1399 - lr: 1.0000e-04 - 266ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0599 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1410 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0561 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1409 - lr: 1.0000e-05 - 241ms/epoch - 4ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0576 - val_loss: 0.0330 - val_mse: 0.0330 - val_mae: 0.1407 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0576 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1403 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 24/500

Epoch 00024: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00024: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0573 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1400 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0589 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1403 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0558 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1410 - lr: 1.0000e-05 - 236ms/epoch - 4ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0605 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1412 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0595 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1416 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0584 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1416 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0591 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1424 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0592 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1420 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0568 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1423 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0552 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1426 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0592 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1437 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0568 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1441 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0576 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1444 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0558 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1444 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0549 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1437 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0574 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1438 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0543 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1424 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0554 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1411 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0566 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1405 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0572 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1392 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0561 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1400 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0545 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1403 - lr: 1.0000e-05 - 256ms/epoch - 4ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0588 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1404 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0564 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1395 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0553 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1390 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0567 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1388 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0551 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1392 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0542 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1392 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0549 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1399 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0537 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1402 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0581 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1403 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0524 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1405 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0526 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1407 - lr: 1.0000e-05 - 257ms/epoch - 4ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0556 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1404 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0536 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1397 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0553 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1388 - lr: 1.0000e-05 - 241ms/epoch - 4ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0529 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1382 - lr: 1.0000e-05 - 283ms/epoch - 5ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.03175
58/58 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0579 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1384 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 62/500

Epoch 00062: val_loss improved from 0.03175 to 0.03167, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0558 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1375 - lr: 1.0000e-05 - 441ms/epoch - 8ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0564 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1376 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0523 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1383 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0548 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1382 - lr: 1.0000e-05 - 303ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0534 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1379 - lr: 1.0000e-05 - 276ms/epoch - 5ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0582 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1382 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0565 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1401 - lr: 1.0000e-05 - 236ms/epoch - 4ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0529 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1405 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0557 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1404 - lr: 1.0000e-05 - 329ms/epoch - 6ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0560 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1402 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0522 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1391 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0523 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1402 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0540 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1409 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0526 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1411 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0541 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1416 - lr: 1.0000e-05 - 239ms/epoch - 4ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0531 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1408 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0511 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1387 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0516 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1384 - lr: 1.0000e-05 - 255ms/epoch - 4ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0545 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1406 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0536 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1390 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0529 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1380 - lr: 1.0000e-05 - 246ms/epoch - 4ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0511 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1380 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 84/500

Epoch 00084: val_loss did not improve from 0.03167
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0547 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1393 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 85/500

Epoch 00085: val_loss improved from 0.03167 to 0.03129, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0519 - val_loss: 0.0313 - val_mse: 0.0313 - val_mae: 0.1369 - lr: 1.0000e-05 - 428ms/epoch - 7ms/step
Epoch 86/500

Epoch 00086: val_loss did not improve from 0.03129
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0519 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1382 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 87/500

Epoch 00087: val_loss did not improve from 0.03129
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0512 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1397 - lr: 1.0000e-05 - 300ms/epoch - 5ms/step
Epoch 88/500

Epoch 00088: val_loss did not improve from 0.03129
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0530 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1402 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 89/500

Epoch 00089: val_loss did not improve from 0.03129
58/58 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0533 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1394 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 90/500

Epoch 00090: val_loss did not improve from 0.03129
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0536 - val_loss: 0.0313 - val_mse: 0.0313 - val_mae: 0.1369 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 91/500

Epoch 00091: val_loss improved from 0.03129 to 0.03083, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0522 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1357 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 92/500

Epoch 00092: val_loss improved from 0.03083 to 0.03053, saving model to LSTM3.h5
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0515 - val_loss: 0.0305 - val_mse: 0.0305 - val_mae: 0.1349 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 93/500

Epoch 00093: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0519 - val_loss: 0.0313 - val_mse: 0.0313 - val_mae: 0.1371 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 94/500

Epoch 00094: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0518 - val_loss: 0.0314 - val_mse: 0.0314 - val_mae: 0.1373 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 95/500

Epoch 00095: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0516 - val_loss: 0.0309 - val_mse: 0.0309 - val_mae: 0.1360 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 96/500

Epoch 00096: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0534 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1361 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 97/500

Epoch 00097: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0531 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1382 - lr: 1.0000e-05 - 260ms/epoch - 4ms/step
Epoch 98/500

Epoch 00098: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0512 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1361 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 99/500

Epoch 00099: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0515 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1375 - lr: 1.0000e-05 - 246ms/epoch - 4ms/step
Epoch 100/500

Epoch 00100: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0516 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1377 - lr: 1.0000e-05 - 255ms/epoch - 4ms/step
Epoch 101/500

Epoch 00101: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0521 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1381 - lr: 1.0000e-05 - 299ms/epoch - 5ms/step
Epoch 102/500

Epoch 00102: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0528 - val_loss: 0.0313 - val_mse: 0.0313 - val_mae: 0.1369 - lr: 1.0000e-05 - 257ms/epoch - 4ms/step
Epoch 103/500

Epoch 00103: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0520 - val_loss: 0.0314 - val_mse: 0.0314 - val_mae: 0.1373 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 104/500

Epoch 00104: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0495 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1386 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 105/500

Epoch 00105: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0516 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1386 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 106/500

Epoch 00106: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0523 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1377 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 107/500

Epoch 00107: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0528 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1383 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 108/500

Epoch 00108: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0499 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1377 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 109/500

Epoch 00109: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0498 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1383 - lr: 1.0000e-05 - 301ms/epoch - 5ms/step
Epoch 110/500

Epoch 00110: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0471 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1383 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 111/500

Epoch 00111: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0511 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1383 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 112/500

Epoch 00112: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0497 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1382 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 113/500

Epoch 00113: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0475 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1388 - lr: 1.0000e-05 - 341ms/epoch - 6ms/step
Epoch 114/500

Epoch 00114: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0314 - val_mse: 0.0314 - val_mae: 0.1371 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 115/500

Epoch 00115: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0478 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1386 - lr: 1.0000e-05 - 260ms/epoch - 4ms/step
Epoch 116/500

Epoch 00116: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0486 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1403 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 117/500

Epoch 00117: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0497 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1420 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 118/500

Epoch 00118: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0528 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1425 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 119/500

Epoch 00119: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0479 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1416 - lr: 1.0000e-05 - 276ms/epoch - 5ms/step
Epoch 120/500

Epoch 00120: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0493 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1404 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 121/500

Epoch 00121: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0506 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1409 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 122/500

Epoch 00122: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0514 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1418 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 123/500

Epoch 00123: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0491 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1426 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 124/500

Epoch 00124: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0496 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1419 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 125/500

Epoch 00125: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0491 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1424 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 126/500

Epoch 00126: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0501 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1420 - lr: 1.0000e-05 - 305ms/epoch - 5ms/step
Epoch 127/500

Epoch 00127: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0504 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1425 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 128/500

Epoch 00128: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0495 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1401 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 129/500

Epoch 00129: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0502 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1405 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 130/500

Epoch 00130: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0506 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1396 - lr: 1.0000e-05 - 311ms/epoch - 5ms/step
Epoch 131/500

Epoch 00131: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0496 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1398 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 132/500

Epoch 00132: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1409 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 133/500

Epoch 00133: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0498 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1404 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 134/500

Epoch 00134: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0473 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1418 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 135/500

Epoch 00135: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0487 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1432 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 136/500

Epoch 00136: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0496 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1446 - lr: 1.0000e-05 - 241ms/epoch - 4ms/step
Epoch 137/500

Epoch 00137: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0501 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1438 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 138/500

Epoch 00138: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0481 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1437 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 139/500

Epoch 00139: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0506 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1441 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 140/500

Epoch 00140: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0477 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1427 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 141/500

Epoch 00141: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0483 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1424 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 142/500

Epoch 00142: val_loss did not improve from 0.03053
58/58 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0485 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1422 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 00142: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658

WMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 24.586224407987817 
RMSE:	 4.958449798877449 
MAPE:	 3.970226889097132

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	51.49% Accuracy
MSE:	 207.2547601932076 
RMSE:	 14.3963453762824 
MAPE:	 12.894635987621164

KAMA
Prediction vs Close:		50.75% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 23.743754657069395 
RMSE:	 4.872756371610364 
MAPE:	 3.7850733762502107

MIDPOINT
Prediction vs Close:		50.0% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 35.62442093531873 
RMSE:	 5.968619684258559 
MAPE:	 5.0490603478808165
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.36 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4414.515, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3944.062, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.38 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3715.173, Time=0.06 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3577.471, Time=0.10 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.42 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.58 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3579.471, Time=0.18 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.155 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1784.736
Date:                Sun, 12 Dec 2021   AIC                           3577.471
Time:                        15:07:20   BIC                           3596.235
Sample:                             0   HQIC                          3584.677
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.844      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.861      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.862      0.000      -0.410      -0.387
sigma2         4.9242      0.023    215.469      0.000       4.879       4.969
===================================================================================
Ljung-Box (L1) (Q):                  14.55   Jarque-Bera (JB):           2468024.38
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       274.15
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.11354, saving model to LSTM3.h5
43/43 - 2s - loss: 0.5101 - mse: 0.5101 - mae: 0.5223 - val_loss: 0.1135 - val_mse: 0.1135 - val_mae: 0.3053 - lr: 0.0010 - 2s/epoch - 53ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.11354 to 0.05364, saving model to LSTM3.h5
43/43 - 0s - loss: 0.0361 - mse: 0.0361 - mae: 0.1535 - val_loss: 0.0536 - val_mse: 0.0536 - val_mae: 0.2042 - lr: 0.0010 - 214ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.05364 to 0.04216, saving model to LSTM3.h5
43/43 - 0s - loss: 0.0328 - mse: 0.0328 - mae: 0.1459 - val_loss: 0.0422 - val_mse: 0.0422 - val_mae: 0.1788 - lr: 0.0010 - 236ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.04216 to 0.03063, saving model to LSTM3.h5
43/43 - 0s - loss: 0.0282 - mse: 0.0282 - mae: 0.1364 - val_loss: 0.0306 - val_mse: 0.0306 - val_mae: 0.1492 - lr: 0.0010 - 244ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.03063 to 0.02455, saving model to LSTM3.h5
43/43 - 0s - loss: 0.0227 - mse: 0.0227 - mae: 0.1199 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1304 - lr: 0.0010 - 224ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.02455
43/43 - 0s - loss: 0.0192 - mse: 0.0192 - mae: 0.1128 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1385 - lr: 0.0010 - 181ms/epoch - 4ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.02455
43/43 - 0s - loss: 0.0182 - mse: 0.0182 - mae: 0.1073 - val_loss: 0.0306 - val_mse: 0.0306 - val_mae: 0.1483 - lr: 0.0010 - 212ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.02455
43/43 - 0s - loss: 0.0170 - mse: 0.0170 - mae: 0.1051 - val_loss: 0.0278 - val_mse: 0.0278 - val_mae: 0.1389 - lr: 0.0010 - 211ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.02455 to 0.01847, saving model to LSTM3.h5
43/43 - 0s - loss: 0.0149 - mse: 0.0149 - mae: 0.0974 - val_loss: 0.0185 - val_mse: 0.0185 - val_mae: 0.1060 - lr: 0.0010 - 221ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0128 - mse: 0.0128 - mae: 0.0908 - val_loss: 0.0191 - val_mse: 0.0191 - val_mae: 0.1078 - lr: 0.0010 - 212ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0117 - mse: 0.0117 - mae: 0.0856 - val_loss: 0.0205 - val_mse: 0.0205 - val_mae: 0.1123 - lr: 0.0010 - 179ms/epoch - 4ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0105 - mse: 0.0105 - mae: 0.0826 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1476 - lr: 0.0010 - 235ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0093 - mse: 0.0093 - mae: 0.0758 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1261 - lr: 0.0010 - 186ms/epoch - 4ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00014: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0088 - mse: 0.0088 - mae: 0.0740 - val_loss: 0.0260 - val_mse: 0.0260 - val_mae: 0.1306 - lr: 0.0010 - 183ms/epoch - 4ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0681 - val_loss: 0.0262 - val_mse: 0.0262 - val_mae: 0.1319 - lr: 1.0000e-04 - 197ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0692 - val_loss: 0.0258 - val_mse: 0.0258 - val_mae: 0.1309 - lr: 1.0000e-04 - 181ms/epoch - 4ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0661 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1338 - lr: 1.0000e-04 - 235ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0650 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1384 - lr: 1.0000e-04 - 224ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00019: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0653 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1396 - lr: 1.0000e-04 - 202ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0643 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1399 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0679 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1400 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0610 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1403 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0645 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1407 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00024: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0647 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1408 - lr: 1.0000e-05 - 176ms/epoch - 4ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0632 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1408 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0639 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1403 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0662 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1405 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0663 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1402 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0652 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1406 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0631 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1412 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0633 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1414 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0630 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1416 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0610 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1418 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0646 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1418 - lr: 1.0000e-05 - 180ms/epoch - 4ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0627 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1413 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0644 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1412 - lr: 1.0000e-05 - 206ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0654 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1405 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0636 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1401 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0614 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1400 - lr: 1.0000e-05 - 210ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0642 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1404 - lr: 1.0000e-05 - 176ms/epoch - 4ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0655 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1403 - lr: 1.0000e-05 - 202ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0680 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1398 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0646 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1400 - lr: 1.0000e-05 - 203ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0652 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1400 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0652 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1397 - lr: 1.0000e-05 - 203ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0653 - val_loss: 0.0277 - val_mse: 0.0277 - val_mae: 0.1383 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0609 - val_loss: 0.0277 - val_mse: 0.0277 - val_mae: 0.1383 - lr: 1.0000e-05 - 237ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0622 - val_loss: 0.0277 - val_mse: 0.0277 - val_mae: 0.1383 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0618 - val_loss: 0.0277 - val_mse: 0.0277 - val_mae: 0.1383 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0638 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1393 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0654 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1398 - lr: 1.0000e-05 - 198ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0597 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1406 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0619 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1408 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0623 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1415 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0629 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1415 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0626 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1404 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0606 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1402 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0633 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1395 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.01847
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0613 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1399 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 00059: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658

WMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 24.586224407987817 
RMSE:	 4.958449798877449 
MAPE:	 3.970226889097132

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	51.49% Accuracy
MSE:	 207.2547601932076 
RMSE:	 14.3963453762824 
MAPE:	 12.894635987621164

KAMA
Prediction vs Close:		50.75% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 23.743754657069395 
RMSE:	 4.872756371610364 
MAPE:	 3.7850733762502107

MIDPOINT
Prediction vs Close:		50.0% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 35.62442093531873 
RMSE:	 5.968619684258559 
MAPE:	 5.0490603478808165

T3
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 103.73535640918065 
RMSE:	 10.185055542763655 
MAPE:	 8.016244139827235
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.47 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4352.703, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3889.412, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.26 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3689.930, Time=0.05 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3574.245, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.17 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.77 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3576.245, Time=0.17 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.053 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1783.123
Date:                Sun, 12 Dec 2021   AIC                           3574.245
Time:                        15:08:46   BIC                           3593.008
Sample:                             0   HQIC                          3581.451
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1480      0.004   -302.430      0.000      -1.155      -1.141
ar.L2         -0.8300      0.008    -99.682      0.000      -0.846      -0.814
ar.L3         -0.3687      0.007    -50.527      0.000      -0.383      -0.354
sigma2         4.9055      0.028    175.970      0.000       4.851       4.960
===================================================================================
Ljung-Box (L1) (Q):                  11.61   Jarque-Bera (JB):           1261976.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.16   Skew:                             2.52
Prob(H) (two-sided):                  0.00   Kurtosis:                       196.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.10461, saving model to LSTM3.h5
90/90 - 2s - loss: 0.0205 - mse: 0.0205 - mae: 0.1144 - val_loss: 0.1046 - val_mse: 0.1046 - val_mae: 0.2573 - lr: 0.0010 - 2s/epoch - 27ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.10461
90/90 - 0s - loss: 0.0231 - mse: 0.0231 - mae: 0.1277 - val_loss: 0.1193 - val_mse: 0.1193 - val_mae: 0.2715 - lr: 0.0010 - 362ms/epoch - 4ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.10461 to 0.07773, saving model to LSTM3.h5
90/90 - 0s - loss: 0.0539 - mse: 0.0539 - mae: 0.1890 - val_loss: 0.0777 - val_mse: 0.0777 - val_mae: 0.2154 - lr: 0.0010 - 378ms/epoch - 4ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.07773 to 0.04901, saving model to LSTM3.h5
90/90 - 0s - loss: 0.0338 - mse: 0.0338 - mae: 0.1354 - val_loss: 0.0490 - val_mse: 0.0490 - val_mae: 0.1713 - lr: 0.0010 - 382ms/epoch - 4ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0678 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.1756 - lr: 0.0010 - 364ms/epoch - 4ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0622 - val_loss: 0.0663 - val_mse: 0.0663 - val_mae: 0.2078 - lr: 0.0010 - 376ms/epoch - 4ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0610 - val_loss: 0.0561 - val_mse: 0.0561 - val_mae: 0.1890 - lr: 0.0010 - 359ms/epoch - 4ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0627 - val_loss: 0.0839 - val_mse: 0.0839 - val_mae: 0.2436 - lr: 0.0010 - 380ms/epoch - 4ms/step
Epoch 9/500

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00009: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0646 - val_loss: 0.0571 - val_mse: 0.0571 - val_mae: 0.1908 - lr: 0.0010 - 374ms/epoch - 4ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0101 - mse: 0.0101 - mae: 0.0831 - val_loss: 0.0584 - val_mse: 0.0584 - val_mae: 0.1948 - lr: 1.0000e-04 - 372ms/epoch - 4ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0715 - val_loss: 0.0543 - val_mse: 0.0543 - val_mae: 0.1852 - lr: 1.0000e-04 - 354ms/epoch - 4ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0662 - val_loss: 0.0541 - val_mse: 0.0541 - val_mae: 0.1841 - lr: 1.0000e-04 - 381ms/epoch - 4ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0671 - val_loss: 0.0554 - val_mse: 0.0554 - val_mae: 0.1864 - lr: 1.0000e-04 - 369ms/epoch - 4ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00014: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0632 - val_loss: 0.0577 - val_mse: 0.0577 - val_mae: 0.1909 - lr: 1.0000e-04 - 439ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0564 - val_loss: 0.0580 - val_mse: 0.0580 - val_mae: 0.1916 - lr: 1.0000e-05 - 393ms/epoch - 4ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0551 - val_loss: 0.0582 - val_mse: 0.0582 - val_mae: 0.1920 - lr: 1.0000e-05 - 379ms/epoch - 4ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0600 - val_loss: 0.0585 - val_mse: 0.0585 - val_mae: 0.1926 - lr: 1.0000e-05 - 379ms/epoch - 4ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0583 - val_loss: 0.0589 - val_mse: 0.0589 - val_mae: 0.1934 - lr: 1.0000e-05 - 354ms/epoch - 4ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00019: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0595 - val_loss: 0.0593 - val_mse: 0.0593 - val_mae: 0.1942 - lr: 1.0000e-05 - 364ms/epoch - 4ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0582 - val_loss: 0.0594 - val_mse: 0.0594 - val_mae: 0.1944 - lr: 1.0000e-05 - 358ms/epoch - 4ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0566 - val_loss: 0.0594 - val_mse: 0.0594 - val_mae: 0.1945 - lr: 1.0000e-05 - 364ms/epoch - 4ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0549 - val_loss: 0.0600 - val_mse: 0.0600 - val_mae: 0.1956 - lr: 1.0000e-05 - 422ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0527 - val_loss: 0.0603 - val_mse: 0.0603 - val_mae: 0.1961 - lr: 1.0000e-05 - 358ms/epoch - 4ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0566 - val_loss: 0.0608 - val_mse: 0.0608 - val_mae: 0.1973 - lr: 1.0000e-05 - 364ms/epoch - 4ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0565 - val_loss: 0.0612 - val_mse: 0.0612 - val_mae: 0.1980 - lr: 1.0000e-05 - 354ms/epoch - 4ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0554 - val_loss: 0.0614 - val_mse: 0.0614 - val_mae: 0.1985 - lr: 1.0000e-05 - 374ms/epoch - 4ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0558 - val_loss: 0.0620 - val_mse: 0.0620 - val_mae: 0.1996 - lr: 1.0000e-05 - 364ms/epoch - 4ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0545 - val_loss: 0.0621 - val_mse: 0.0621 - val_mae: 0.2000 - lr: 1.0000e-05 - 372ms/epoch - 4ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0559 - val_loss: 0.0623 - val_mse: 0.0623 - val_mae: 0.2003 - lr: 1.0000e-05 - 355ms/epoch - 4ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0553 - val_loss: 0.0623 - val_mse: 0.0623 - val_mae: 0.2003 - lr: 1.0000e-05 - 394ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0544 - val_loss: 0.0624 - val_mse: 0.0624 - val_mae: 0.2003 - lr: 1.0000e-05 - 354ms/epoch - 4ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0550 - val_loss: 0.0624 - val_mse: 0.0624 - val_mae: 0.2003 - lr: 1.0000e-05 - 359ms/epoch - 4ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0560 - val_loss: 0.0625 - val_mse: 0.0625 - val_mae: 0.2006 - lr: 1.0000e-05 - 382ms/epoch - 4ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0545 - val_loss: 0.0626 - val_mse: 0.0626 - val_mae: 0.2008 - lr: 1.0000e-05 - 353ms/epoch - 4ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0543 - val_loss: 0.0629 - val_mse: 0.0629 - val_mae: 0.2012 - lr: 1.0000e-05 - 362ms/epoch - 4ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0535 - val_loss: 0.0632 - val_mse: 0.0632 - val_mae: 0.2019 - lr: 1.0000e-05 - 386ms/epoch - 4ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0519 - val_loss: 0.0634 - val_mse: 0.0634 - val_mae: 0.2023 - lr: 1.0000e-05 - 367ms/epoch - 4ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0529 - val_loss: 0.0638 - val_mse: 0.0638 - val_mae: 0.2029 - lr: 1.0000e-05 - 446ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0551 - val_loss: 0.0639 - val_mse: 0.0639 - val_mae: 0.2032 - lr: 1.0000e-05 - 440ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0537 - val_loss: 0.0646 - val_mse: 0.0646 - val_mae: 0.2046 - lr: 1.0000e-05 - 364ms/epoch - 4ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0532 - val_loss: 0.0647 - val_mse: 0.0647 - val_mae: 0.2047 - lr: 1.0000e-05 - 382ms/epoch - 4ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0527 - val_loss: 0.0653 - val_mse: 0.0653 - val_mae: 0.2060 - lr: 1.0000e-05 - 394ms/epoch - 4ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0533 - val_loss: 0.0657 - val_mse: 0.0657 - val_mae: 0.2068 - lr: 1.0000e-05 - 364ms/epoch - 4ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0501 - val_loss: 0.0663 - val_mse: 0.0663 - val_mae: 0.2079 - lr: 1.0000e-05 - 360ms/epoch - 4ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0512 - val_loss: 0.0664 - val_mse: 0.0664 - val_mae: 0.2082 - lr: 1.0000e-05 - 354ms/epoch - 4ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0531 - val_loss: 0.0665 - val_mse: 0.0665 - val_mae: 0.2083 - lr: 1.0000e-05 - 365ms/epoch - 4ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0524 - val_loss: 0.0670 - val_mse: 0.0670 - val_mae: 0.2093 - lr: 1.0000e-05 - 367ms/epoch - 4ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0509 - val_loss: 0.0678 - val_mse: 0.0678 - val_mae: 0.2108 - lr: 1.0000e-05 - 352ms/epoch - 4ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0493 - val_loss: 0.0678 - val_mse: 0.0678 - val_mae: 0.2109 - lr: 1.0000e-05 - 360ms/epoch - 4ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.0683 - val_mse: 0.0683 - val_mae: 0.2118 - lr: 1.0000e-05 - 370ms/epoch - 4ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0516 - val_loss: 0.0686 - val_mse: 0.0686 - val_mae: 0.2123 - lr: 1.0000e-05 - 358ms/epoch - 4ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0509 - val_loss: 0.0690 - val_mse: 0.0690 - val_mae: 0.2132 - lr: 1.0000e-05 - 360ms/epoch - 4ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0513 - val_loss: 0.0694 - val_mse: 0.0694 - val_mae: 0.2139 - lr: 1.0000e-05 - 379ms/epoch - 4ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.04901
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0515 - val_loss: 0.0698 - val_mse: 0.0698 - val_mae: 0.2147 - lr: 1.0000e-05 - 363ms/epoch - 4ms/step
Epoch 00054: early stopping
SMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.725767438505336 
RMSE:	 5.7206439706125165 
MAPE:	 4.798603095387009

EMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.9% Accuracy
MSE:	 143.9522591181831 
RMSE:	 11.998010631691534 
MAPE:	 10.07848404711658

WMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 24.586224407987817 
RMSE:	 4.958449798877449 
MAPE:	 3.970226889097132

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	51.49% Accuracy
MSE:	 207.2547601932076 
RMSE:	 14.3963453762824 
MAPE:	 12.894635987621164

KAMA
Prediction vs Close:		50.75% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 23.743754657069395 
RMSE:	 4.872756371610364 
MAPE:	 3.7850733762502107

MIDPOINT
Prediction vs Close:		50.0% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 35.62442093531873 
RMSE:	 5.968619684258559 
MAPE:	 5.0490603478808165

T3
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 103.73535640918065 
RMSE:	 10.185055542763655 
MAPE:	 8.016244139827235

TEMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	47.76% Accuracy
MSE:	 39.894741171517865 
RMSE:	 6.316228397668807 
MAPE:	 5.481705479796751
Runtime: mins: 12.72821348606667

Architecture Used

In [94]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
In [95]:
img = cv2.imread('Experiment3.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[95]:
<matplotlib.image.AxesImage at 0x7fa5c4399110>

Model Plots

In [96]:
for i in range(len(list(simulation3.keys()))):
  SIM = list(simulation3.keys())[i]
  plot_train(simulation3,SIM)
  plot_test(simulation3,SIM)
----- Train RMSE for SMA ----- 9.155996008701845
----- Train_MSE_LSTM for SMA ----- 83.8322629113641
----- Train MAE LSTM for SMA ----- 8.009066904332526
----- Test RMSE for SMA----- 5.7206439706125165
----- Test_MSE_LSTM for SMA----- 32.725767438505336
----- Test_MAE_LSTM for SMA----- 4.798603095387009
----- Train RMSE for EMA ----- 10.577937809273275
----- Train_MSE_LSTM for EMA ----- 111.89276829685309
----- Train MAE LSTM for EMA ----- 9.432194934544697
----- Test RMSE for EMA----- 11.998010631691534
----- Test_MSE_LSTM for EMA----- 143.9522591181831
----- Test_MAE_LSTM for EMA----- 10.07848404711658
----- Train RMSE for WMA ----- 11.6464497844428
----- Train_MSE_LSTM for WMA ----- 135.63979258154774
----- Train MAE LSTM for WMA ----- 10.44059237611322
----- Test RMSE for WMA----- 4.958449798877449
----- Test_MSE_LSTM for WMA----- 24.586224407987817
----- Test_MAE_LSTM for WMA----- 3.970226889097132
----- Train RMSE for DEMA ----- 12.798003878729565
----- Train_MSE_LSTM for DEMA ----- 163.78890327997698
----- Train MAE LSTM for DEMA ----- 11.593951105370675
----- Test RMSE for DEMA----- 14.3963453762824
----- Test_MSE_LSTM for DEMA----- 207.2547601932076
----- Test_MAE_LSTM for DEMA----- 12.894635987621164
----- Train RMSE for KAMA ----- 10.917779337092348
----- Train_MSE_LSTM for KAMA ----- 119.19790565344064
----- Train MAE LSTM for KAMA ----- 9.91727143279415
----- Test RMSE for KAMA----- 4.872756371610364
----- Test_MSE_LSTM for KAMA----- 23.743754657069395
----- Test_MAE_LSTM for KAMA----- 3.7850733762502107
----- Train RMSE for MIDPOINT ----- 9.672965115616142
----- Train_MSE_LSTM for MIDPOINT ----- 93.56625412792681
----- Train MAE LSTM for MIDPOINT ----- 8.607510758185814
----- Test RMSE for MIDPOINT----- 5.968619684258559
----- Test_MSE_LSTM for MIDPOINT----- 35.62442093531873
----- Test_MAE_LSTM for MIDPOINT----- 5.0490603478808165
----- Train RMSE for T3 ----- 12.322513146591183
----- Train_MSE_LSTM for T3 ----- 151.84433024791252
----- Train MAE LSTM for T3 ----- 11.162838602510032
----- Test RMSE for T3----- 10.185055542763655
----- Test_MSE_LSTM for T3----- 103.73535640918065
----- Test_MAE_LSTM for T3----- 8.016244139827235
----- Train RMSE for TEMA ----- 7.464720413063123
----- Train_MSE_LSTM for TEMA ----- 55.72205084520128
----- Train MAE LSTM for TEMA ----- 5.129409694220032
----- Test RMSE for TEMA----- 6.316228397668807
----- Test_MSE_LSTM for TEMA----- 39.894741171517865
----- Test_MAE_LSTM for TEMA----- 5.481705479796751

Univariate Arima Multistep MutiVariate LSTM Hybrid Model Experiment 4

From the above experiments it is evident that with Higher moving averages the loss plots show unreoresented data and underfitting, hence keeping only the MA's that have smaller periods like T3 OR TRIMA. Going forward EMA, WMA & DEMA will be ignored.

In [97]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det = 20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # # Option 1
    # # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    # model.add(Dense(units=64,activation='relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    # ## Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()


    # # option 2
    # model = Sequential()
    # model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    # model.add(Dense(64))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()




    # # Option 3
    # # define custom activation
    # # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    # class Double_Tanh(Activation):
    #     def __init__(self, activation, **kwargs):
    #         super(Double_Tanh, self).__init__(activation, **kwargs)
    #         self.__name__ = 'double_tanh'

    # def double_tanh(x):
    #     return (K.tanh(x) * 2)

    # get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
    #     # Model Generation
    # model = Sequential()
    # #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    # model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    # model.add(Dense(1))
    # model.add(Activation(double_tanh))
    # model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    model = Sequential()
    model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(input_dim, feature_size)))
    model.add(LSTM(units=int(lstm_len/2)))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM4.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = (y_scaler.inverse_transform(predictiontr)-det).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte =( y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [98]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation4 = {}
    imgfile = 'Experiment4'
    for ma in optimized_period:
              print(ma)
              print(functions[ma])
              print ( int( optimized_period[ma]))
            # if ma == 'SMA':
              low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
              low_vol = low_vol.fillna(0)
              low_vol_data = df['close']
              high_vol = pd.DataFrame()
              df2 = df.copy()
              for i in df2.columns:
                if i in low_vol.columns:
                  high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
              high_vol_data = df['close']
              ## *****************************************************
              # Generate ARIMA and LSTM predictions
              print('\nWorking on ' + ma + ' predictions')
              try:
                print('parameters used : ', train_len, test_len)
                low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima(low_vol,low_vol_data, train_len, test_len)
              except:
                  print('ARIMA error, skipping to next MA type')
                  continue
              Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
              final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
              mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
              rmse_ftr = mse_ftr ** 0.5
              mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
              mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

              final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
              mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
              rmse = mse ** 0.5
              mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
              # Generate prediction accuracy
              actual = df['close'].tail(test_len).values
              result_1 = []
              result_2 = []
              for i in range(1, len(final_prediction)):
                  # Compare prediction to previous close price
                  if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                      result_1.append(1)
                  elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                      result_1.append(1)
                  else:
                      result_1.append(0)

                  # Compare prediction to previous prediction
                  if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                      result_2.append(1)
                  elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                      result_2.append(1)
                  else:
                      result_2.append(0)

              accuracy_1 = np.mean(result_1)
              accuracy_2 = np.mean(result_2)

              simulation4[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                            'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                            'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                            'rmse': rmse_ftr, 'mae' : mae_ftr},
                                'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                          'rmse': rmse, 'mae': mae },
                                'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

              # save simulation data here as checkpoint
              with open('simulation4_data.json', 'w') as fp:
                  json.dump(simulation4, fp)

              for ma in simulation4.keys():
                  print('\n' + ma)
                  print('Prediction vs Close:\t\t' + str(round(100*simulation4[ma]['accuracy']['prediction vs close'], 2))
                        + '% Accuracy')
                  print('Prediction vs Prediction:\t' + str(round(100*simulation4[ma]['accuracy']['prediction vs prediction'], 2))
                        + '% Accuracy')
                  print('MSE:\t', simulation4[ma]['final']['mse'],
                        '\nRMSE:\t', simulation4[ma]['final']['rmse'],
                        '\nMAPE:\t', simulation4[ma]['final']['mae'])#,
                        # '\nMAPE:\t', simulation[ma]['final']['mape'])
            # else:
            #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.54 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4157.020, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3687.148, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.19 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3458.651, Time=0.08 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3322.133, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.76 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.82 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3324.133, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.749 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1657.067
Date:                Sun, 12 Dec 2021   AIC                           3322.133
Time:                        15:18:11   BIC                           3340.897
Sample:                             0   HQIC                          3329.339
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1966      0.003   -387.226      0.000      -1.203      -1.191
ar.L2         -0.8952      0.006   -138.692      0.000      -0.908      -0.883
ar.L3         -0.3968      0.006    -68.284      0.000      -0.408      -0.385
sigma2         3.5858      0.017    214.535      0.000       3.553       3.619
===================================================================================
Ljung-Box (L1) (Q):                  14.47   Jarque-Bera (JB):           2428881.42
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       271.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.06157, saving model to LSTM4.h5
48/48 - 4s - loss: 1.3923 - val_loss: 0.0616 - lr: 0.0010 - 4s/epoch - 77ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.06157
48/48 - 0s - loss: 1.2621 - val_loss: 0.0663 - lr: 0.0010 - 263ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.06157
48/48 - 0s - loss: 1.1314 - val_loss: 0.0750 - lr: 0.0010 - 252ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.06157
48/48 - 0s - loss: 1.0246 - val_loss: 0.0868 - lr: 0.0010 - 278ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.9411 - val_loss: 0.0972 - lr: 0.0010 - 255ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8738 - val_loss: 0.1044 - lr: 0.0010 - 246ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8332 - val_loss: 0.1049 - lr: 1.0000e-04 - 243ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8268 - val_loss: 0.1054 - lr: 1.0000e-04 - 275ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8207 - val_loss: 0.1059 - lr: 1.0000e-04 - 243ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8147 - val_loss: 0.1064 - lr: 1.0000e-04 - 267ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8089 - val_loss: 0.1069 - lr: 1.0000e-04 - 261ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8053 - val_loss: 0.1070 - lr: 1.0000e-05 - 246ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8047 - val_loss: 0.1070 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8042 - val_loss: 0.1071 - lr: 1.0000e-05 - 239ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8036 - val_loss: 0.1071 - lr: 1.0000e-05 - 284ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8031 - val_loss: 0.1072 - lr: 1.0000e-05 - 277ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8025 - val_loss: 0.1073 - lr: 1.0000e-05 - 251ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8019 - val_loss: 0.1073 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8014 - val_loss: 0.1074 - lr: 1.0000e-05 - 287ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8008 - val_loss: 0.1075 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.8002 - val_loss: 0.1076 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7997 - val_loss: 0.1076 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7991 - val_loss: 0.1077 - lr: 1.0000e-05 - 318ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7985 - val_loss: 0.1078 - lr: 1.0000e-05 - 254ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7980 - val_loss: 0.1079 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7974 - val_loss: 0.1079 - lr: 1.0000e-05 - 256ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7968 - val_loss: 0.1080 - lr: 1.0000e-05 - 280ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7963 - val_loss: 0.1081 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7957 - val_loss: 0.1082 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7951 - val_loss: 0.1083 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7946 - val_loss: 0.1084 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7940 - val_loss: 0.1084 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7934 - val_loss: 0.1085 - lr: 1.0000e-05 - 239ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7929 - val_loss: 0.1086 - lr: 1.0000e-05 - 257ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7923 - val_loss: 0.1087 - lr: 1.0000e-05 - 254ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7917 - val_loss: 0.1088 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7912 - val_loss: 0.1089 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7906 - val_loss: 0.1090 - lr: 1.0000e-05 - 292ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7901 - val_loss: 0.1091 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7895 - val_loss: 0.1091 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7889 - val_loss: 0.1092 - lr: 1.0000e-05 - 247ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7884 - val_loss: 0.1093 - lr: 1.0000e-05 - 288ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7878 - val_loss: 0.1094 - lr: 1.0000e-05 - 257ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7873 - val_loss: 0.1095 - lr: 1.0000e-05 - 239ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7867 - val_loss: 0.1096 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7862 - val_loss: 0.1097 - lr: 1.0000e-05 - 280ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7856 - val_loss: 0.1098 - lr: 1.0000e-05 - 250ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7850 - val_loss: 0.1099 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7845 - val_loss: 0.1100 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7839 - val_loss: 0.1101 - lr: 1.0000e-05 - 260ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.06157
48/48 - 0s - loss: 0.7834 - val_loss: 0.1102 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.42 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4231.556, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3761.238, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.28 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3532.227, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3394.496, Time=0.10 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.87 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.64 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3396.496, Time=0.22 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 2.687 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1693.248
Date:                Sun, 12 Dec 2021   AIC                           3394.496
Time:                        15:19:44   BIC                           3413.260
Sample:                             0   HQIC                          3401.702
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.569      0.000      -1.204      -1.192
ar.L2         -0.8976      0.006   -139.811      0.000      -0.910      -0.885
ar.L3         -0.3984      0.006    -68.662      0.000      -0.410      -0.387
sigma2         3.9230      0.018    215.372      0.000       3.887       3.959
===================================================================================
Ljung-Box (L1) (Q):                  14.54   Jarque-Bera (JB):           2462173.05
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.82
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05562, saving model to LSTM4.h5
16/16 - 3s - loss: 1.4917 - val_loss: 0.0556 - lr: 0.0010 - 3s/epoch - 218ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.05562 to 0.05481, saving model to LSTM4.h5
16/16 - 0s - loss: 1.3932 - val_loss: 0.0548 - lr: 0.0010 - 146ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.05481 to 0.05412, saving model to LSTM4.h5
16/16 - 0s - loss: 1.2699 - val_loss: 0.0541 - lr: 0.0010 - 129ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.05412 to 0.05405, saving model to LSTM4.h5
16/16 - 0s - loss: 1.1744 - val_loss: 0.0540 - lr: 0.0010 - 131ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.1194 - val_loss: 0.0546 - lr: 0.0010 - 105ms/epoch - 7ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0830 - val_loss: 0.0556 - lr: 0.0010 - 102ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0545 - val_loss: 0.0569 - lr: 0.0010 - 93ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00008: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0300 - val_loss: 0.0583 - lr: 0.0010 - 106ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0154 - val_loss: 0.0584 - lr: 1.0000e-04 - 92ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0133 - val_loss: 0.0586 - lr: 1.0000e-04 - 138ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0112 - val_loss: 0.0587 - lr: 1.0000e-04 - 113ms/epoch - 7ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0091 - val_loss: 0.0589 - lr: 1.0000e-04 - 109ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00013: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0071 - val_loss: 0.0591 - lr: 1.0000e-04 - 111ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0058 - val_loss: 0.0591 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0056 - val_loss: 0.0591 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0053 - val_loss: 0.0591 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0051 - val_loss: 0.0591 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00018: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0049 - val_loss: 0.0592 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0047 - val_loss: 0.0592 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0045 - val_loss: 0.0592 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0043 - val_loss: 0.0592 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0041 - val_loss: 0.0592 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0039 - val_loss: 0.0593 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0037 - val_loss: 0.0593 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0035 - val_loss: 0.0593 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0033 - val_loss: 0.0593 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0030 - val_loss: 0.0593 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0028 - val_loss: 0.0593 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0026 - val_loss: 0.0594 - lr: 1.0000e-05 - 125ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0024 - val_loss: 0.0594 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0022 - val_loss: 0.0594 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0020 - val_loss: 0.0594 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0018 - val_loss: 0.0594 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0016 - val_loss: 0.0595 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0013 - val_loss: 0.0595 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0011 - val_loss: 0.0595 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0009 - val_loss: 0.0595 - lr: 1.0000e-05 - 121ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0007 - val_loss: 0.0596 - lr: 1.0000e-05 - 124ms/epoch - 8ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0005 - val_loss: 0.0596 - lr: 1.0000e-05 - 131ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0003 - val_loss: 0.0596 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05405
16/16 - 0s - loss: 1.0000 - val_loss: 0.0596 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9998 - val_loss: 0.0596 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9996 - val_loss: 0.0597 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9994 - val_loss: 0.0597 - lr: 1.0000e-05 - 124ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9992 - val_loss: 0.0597 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9989 - val_loss: 0.0597 - lr: 1.0000e-05 - 144ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9987 - val_loss: 0.0597 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9985 - val_loss: 0.0598 - lr: 1.0000e-05 - 120ms/epoch - 7ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9983 - val_loss: 0.0598 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9981 - val_loss: 0.0598 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9979 - val_loss: 0.0598 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9976 - val_loss: 0.0599 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9974 - val_loss: 0.0599 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.05405
16/16 - 0s - loss: 0.9972 - val_loss: 0.0599 - lr: 1.0000e-05 - 130ms/epoch - 8ms/step
Epoch 00054: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.43 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4264.089, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3793.930, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.24 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3564.923, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3427.258, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.30 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.46 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3429.258, Time=0.69 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.352 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1709.629
Date:                Sun, 12 Dec 2021   AIC                           3427.258
Time:                        15:21:04   BIC                           3446.021
Sample:                             0   HQIC                          3434.464
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1981      0.003   -389.386      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.699      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.737      0.000      -0.410      -0.387
sigma2         4.0860      0.019    215.311      0.000       4.049       4.123
===================================================================================
Ljung-Box (L1) (Q):                  14.57   Jarque-Bera (JB):           2460901.70
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04594, saving model to LSTM4.h5
17/17 - 4s - loss: 1.3787 - val_loss: 0.0459 - lr: 0.0010 - 4s/epoch - 208ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.3437 - val_loss: 0.0469 - lr: 0.0010 - 93ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.3089 - val_loss: 0.0478 - lr: 0.0010 - 104ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.2753 - val_loss: 0.0487 - lr: 0.0010 - 107ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.2443 - val_loss: 0.0495 - lr: 0.0010 - 104ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.2162 - val_loss: 0.0506 - lr: 0.0010 - 104ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1992 - val_loss: 0.0507 - lr: 1.0000e-04 - 112ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1968 - val_loss: 0.0508 - lr: 1.0000e-04 - 105ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1943 - val_loss: 0.0509 - lr: 1.0000e-04 - 107ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1919 - val_loss: 0.0511 - lr: 1.0000e-04 - 105ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1895 - val_loss: 0.0512 - lr: 1.0000e-04 - 104ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1880 - val_loss: 0.0512 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1878 - val_loss: 0.0512 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1875 - val_loss: 0.0512 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1873 - val_loss: 0.0513 - lr: 1.0000e-05 - 135ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1870 - val_loss: 0.0513 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1868 - val_loss: 0.0513 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1866 - val_loss: 0.0513 - lr: 1.0000e-05 - 125ms/epoch - 7ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1863 - val_loss: 0.0513 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1861 - val_loss: 0.0513 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1859 - val_loss: 0.0514 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1856 - val_loss: 0.0514 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1854 - val_loss: 0.0514 - lr: 1.0000e-05 - 120ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1851 - val_loss: 0.0514 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1849 - val_loss: 0.0514 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1847 - val_loss: 0.0514 - lr: 1.0000e-05 - 123ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1844 - val_loss: 0.0514 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1842 - val_loss: 0.0515 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1839 - val_loss: 0.0515 - lr: 1.0000e-05 - 106ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1837 - val_loss: 0.0515 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1835 - val_loss: 0.0515 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1832 - val_loss: 0.0515 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1830 - val_loss: 0.0516 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1827 - val_loss: 0.0516 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1825 - val_loss: 0.0516 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1823 - val_loss: 0.0516 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1820 - val_loss: 0.0516 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1818 - val_loss: 0.0516 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1815 - val_loss: 0.0517 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1813 - val_loss: 0.0517 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1811 - val_loss: 0.0517 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1808 - val_loss: 0.0517 - lr: 1.0000e-05 - 126ms/epoch - 7ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1806 - val_loss: 0.0517 - lr: 1.0000e-05 - 121ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1803 - val_loss: 0.0518 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1801 - val_loss: 0.0518 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1799 - val_loss: 0.0518 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1796 - val_loss: 0.0518 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1794 - val_loss: 0.0518 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1792 - val_loss: 0.0519 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1789 - val_loss: 0.0519 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04594
17/17 - 0s - loss: 1.1787 - val_loss: 0.0519 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 52.4753296205182 
RMSE:	 7.2439857551294375 
MAPE:	 5.852253139584933
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.44 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4436.126, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3965.317, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3736.589, Time=0.06 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3598.951, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=0.95 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.94 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3600.951, Time=0.18 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.101 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1795.475
Date:                Sun, 12 Dec 2021   AIC                           3598.951
Time:                        15:22:26   BIC                           3617.714
Sample:                             0   HQIC                          3606.157
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1983      0.003   -389.581      0.000      -1.204      -1.192
ar.L2         -0.8973      0.006   -139.732      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.649      0.000      -0.410      -0.387
sigma2         5.0573      0.023    215.292      0.000       5.011       5.103
===================================================================================
Ljung-Box (L1) (Q):                  14.41   Jarque-Bera (JB):           2460553.80
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.89
Prob(H) (two-sided):                  0.00   Kurtosis:                       273.74
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04142, saving model to LSTM4.h5
10/10 - 4s - loss: 1.3181 - val_loss: 0.0414 - lr: 0.0010 - 4s/epoch - 374ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.04142 to 0.04094, saving model to LSTM4.h5
10/10 - 0s - loss: 1.2515 - val_loss: 0.0409 - lr: 0.0010 - 90ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.04094 to 0.04051, saving model to LSTM4.h5
10/10 - 0s - loss: 1.1982 - val_loss: 0.0405 - lr: 0.0010 - 84ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.04051 to 0.04014, saving model to LSTM4.h5
10/10 - 0s - loss: 1.1531 - val_loss: 0.0401 - lr: 0.0010 - 81ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.04014 to 0.03983, saving model to LSTM4.h5
10/10 - 0s - loss: 1.1133 - val_loss: 0.0398 - lr: 0.0010 - 98ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.03983 to 0.03959, saving model to LSTM4.h5
10/10 - 0s - loss: 1.0771 - val_loss: 0.0396 - lr: 0.0010 - 85ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.03959 to 0.03942, saving model to LSTM4.h5
10/10 - 0s - loss: 1.0439 - val_loss: 0.0394 - lr: 0.0010 - 80ms/epoch - 8ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.03942 to 0.03934, saving model to LSTM4.h5
10/10 - 0s - loss: 1.0138 - val_loss: 0.0393 - lr: 0.0010 - 84ms/epoch - 8ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9873 - val_loss: 0.0394 - lr: 0.0010 - 90ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9643 - val_loss: 0.0395 - lr: 0.0010 - 106ms/epoch - 11ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9445 - val_loss: 0.0397 - lr: 0.0010 - 84ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00012: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9272 - val_loss: 0.0400 - lr: 0.0010 - 84ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9162 - val_loss: 0.0400 - lr: 1.0000e-04 - 79ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9148 - val_loss: 0.0401 - lr: 1.0000e-04 - 77ms/epoch - 8ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9134 - val_loss: 0.0401 - lr: 1.0000e-04 - 88ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9120 - val_loss: 0.0401 - lr: 1.0000e-04 - 79ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00017: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9106 - val_loss: 0.0402 - lr: 1.0000e-04 - 71ms/epoch - 7ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9096 - val_loss: 0.0402 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9095 - val_loss: 0.0402 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9093 - val_loss: 0.0402 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9092 - val_loss: 0.0402 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00022: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9091 - val_loss: 0.0402 - lr: 1.0000e-05 - 102ms/epoch - 10ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9089 - val_loss: 0.0402 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9088 - val_loss: 0.0402 - lr: 1.0000e-05 - 88ms/epoch - 9ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9087 - val_loss: 0.0402 - lr: 1.0000e-05 - 107ms/epoch - 11ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9085 - val_loss: 0.0402 - lr: 1.0000e-05 - 81ms/epoch - 8ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9084 - val_loss: 0.0402 - lr: 1.0000e-05 - 81ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9082 - val_loss: 0.0402 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9081 - val_loss: 0.0402 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9079 - val_loss: 0.0402 - lr: 1.0000e-05 - 65ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9078 - val_loss: 0.0402 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9077 - val_loss: 0.0402 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9075 - val_loss: 0.0402 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9074 - val_loss: 0.0403 - lr: 1.0000e-05 - 95ms/epoch - 10ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9072 - val_loss: 0.0403 - lr: 1.0000e-05 - 97ms/epoch - 10ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9071 - val_loss: 0.0403 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9069 - val_loss: 0.0403 - lr: 1.0000e-05 - 79ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9068 - val_loss: 0.0403 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9066 - val_loss: 0.0403 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9065 - val_loss: 0.0403 - lr: 1.0000e-05 - 89ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9064 - val_loss: 0.0403 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9062 - val_loss: 0.0403 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9061 - val_loss: 0.0403 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9059 - val_loss: 0.0403 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9058 - val_loss: 0.0403 - lr: 1.0000e-05 - 92ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9056 - val_loss: 0.0403 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9055 - val_loss: 0.0403 - lr: 1.0000e-05 - 90ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9053 - val_loss: 0.0403 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9052 - val_loss: 0.0403 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9050 - val_loss: 0.0403 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9049 - val_loss: 0.0403 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9047 - val_loss: 0.0403 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9046 - val_loss: 0.0404 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9044 - val_loss: 0.0404 - lr: 1.0000e-05 - 87ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9043 - val_loss: 0.0404 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9041 - val_loss: 0.0404 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9040 - val_loss: 0.0404 - lr: 1.0000e-05 - 93ms/epoch - 9ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.03934
10/10 - 0s - loss: 0.9038 - val_loss: 0.0404 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 00058: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 52.4753296205182 
RMSE:	 7.2439857551294375 
MAPE:	 5.852253139584933

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 146.44755629127866 
RMSE:	 12.10155181335347 
MAPE:	 10.943210296434415
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4190.464, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3724.371, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.29 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3494.154, Time=0.07 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3357.435, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.17 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.74 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3359.435, Time=0.21 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.041 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1674.717
Date:                Sun, 12 Dec 2021   AIC                           3357.435
Time:                        15:23:44   BIC                           3376.198
Sample:                             0   HQIC                          3364.641
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1955      0.003   -381.246      0.000      -1.202      -1.189
ar.L2         -0.8964      0.007   -135.835      0.000      -0.909      -0.883
ar.L3         -0.3971      0.006    -67.229      0.000      -0.409      -0.385
sigma2         3.7466      0.018    211.623      0.000       3.712       3.781
===================================================================================
Ljung-Box (L1) (Q):                  14.20   Jarque-Bera (JB):           2338363.32
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             3.76
Prob(H) (two-sided):                  0.00   Kurtosis:                       266.93
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05310, saving model to LSTM4.h5
45/45 - 4s - loss: 1.2968 - val_loss: 0.0531 - lr: 0.0010 - 4s/epoch - 90ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.05310
45/45 - 0s - loss: 1.0725 - val_loss: 0.0568 - lr: 0.0010 - 315ms/epoch - 7ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.9746 - val_loss: 0.0608 - lr: 0.0010 - 274ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.9066 - val_loss: 0.0645 - lr: 0.0010 - 248ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.8542 - val_loss: 0.0681 - lr: 0.0010 - 269ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.8135 - val_loss: 0.0717 - lr: 0.0010 - 257ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7928 - val_loss: 0.0721 - lr: 1.0000e-04 - 266ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7898 - val_loss: 0.0724 - lr: 1.0000e-04 - 289ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7868 - val_loss: 0.0728 - lr: 1.0000e-04 - 285ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7838 - val_loss: 0.0732 - lr: 1.0000e-04 - 306ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7808 - val_loss: 0.0736 - lr: 1.0000e-04 - 256ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7790 - val_loss: 0.0737 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7787 - val_loss: 0.0737 - lr: 1.0000e-05 - 262ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7784 - val_loss: 0.0738 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7781 - val_loss: 0.0738 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7778 - val_loss: 0.0739 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7774 - val_loss: 0.0739 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7771 - val_loss: 0.0740 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7768 - val_loss: 0.0740 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7765 - val_loss: 0.0740 - lr: 1.0000e-05 - 255ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7761 - val_loss: 0.0741 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7758 - val_loss: 0.0742 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7755 - val_loss: 0.0742 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7751 - val_loss: 0.0743 - lr: 1.0000e-05 - 286ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7748 - val_loss: 0.0743 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7744 - val_loss: 0.0744 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7741 - val_loss: 0.0744 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7737 - val_loss: 0.0745 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7734 - val_loss: 0.0745 - lr: 1.0000e-05 - 260ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7730 - val_loss: 0.0746 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7727 - val_loss: 0.0746 - lr: 1.0000e-05 - 280ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7723 - val_loss: 0.0747 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7720 - val_loss: 0.0748 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7716 - val_loss: 0.0748 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7713 - val_loss: 0.0749 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7709 - val_loss: 0.0749 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7706 - val_loss: 0.0750 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7702 - val_loss: 0.0751 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7698 - val_loss: 0.0751 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7695 - val_loss: 0.0752 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7691 - val_loss: 0.0753 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7688 - val_loss: 0.0753 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7684 - val_loss: 0.0754 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7681 - val_loss: 0.0755 - lr: 1.0000e-05 - 237ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7677 - val_loss: 0.0755 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7673 - val_loss: 0.0756 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7670 - val_loss: 0.0757 - lr: 1.0000e-05 - 239ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7666 - val_loss: 0.0757 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7663 - val_loss: 0.0758 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7659 - val_loss: 0.0759 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05310
45/45 - 0s - loss: 0.7655 - val_loss: 0.0759 - lr: 1.0000e-05 - 294ms/epoch - 7ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 52.4753296205182 
RMSE:	 7.2439857551294375 
MAPE:	 5.852253139584933

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 146.44755629127866 
RMSE:	 12.10155181335347 
MAPE:	 10.943210296434415

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 19.64215945229788 
RMSE:	 4.4319475913302355 
MAPE:	 3.5686191181651687
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.37 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4212.289, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3747.746, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.23 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3523.401, Time=0.08 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3387.759, Time=0.12 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.30 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.88 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3389.758, Time=0.24 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.287 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1689.879
Date:                Sun, 12 Dec 2021   AIC                           3387.759
Time:                        15:25:11   BIC                           3406.522
Sample:                             0   HQIC                          3394.964
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1878      0.003   -345.315      0.000      -1.195      -1.181
ar.L2         -0.8876      0.007   -121.809      0.000      -0.902      -0.873
ar.L3         -0.3957      0.007    -60.127      0.000      -0.409      -0.383
sigma2         3.8904      0.020    193.404      0.000       3.851       3.930
===================================================================================
Ljung-Box (L1) (Q):                  13.21   Jarque-Bera (JB):           1659080.01
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.08   Skew:                             3.28
Prob(H) (two-sided):                  0.00   Kurtosis:                       225.31
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04095, saving model to LSTM4.h5
58/58 - 4s - loss: 1.2983 - val_loss: 0.0410 - lr: 0.0010 - 4s/epoch - 63ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04095
58/58 - 0s - loss: 1.1355 - val_loss: 0.0436 - lr: 0.0010 - 310ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04095
58/58 - 0s - loss: 1.0175 - val_loss: 0.0468 - lr: 0.0010 - 329ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.9394 - val_loss: 0.0508 - lr: 0.0010 - 284ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8828 - val_loss: 0.0556 - lr: 0.0010 - 318ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8400 - val_loss: 0.0610 - lr: 0.0010 - 311ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8176 - val_loss: 0.0616 - lr: 1.0000e-04 - 310ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8144 - val_loss: 0.0622 - lr: 1.0000e-04 - 322ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8111 - val_loss: 0.0628 - lr: 1.0000e-04 - 332ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8078 - val_loss: 0.0634 - lr: 1.0000e-04 - 325ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8045 - val_loss: 0.0640 - lr: 1.0000e-04 - 309ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8025 - val_loss: 0.0641 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8022 - val_loss: 0.0641 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8018 - val_loss: 0.0642 - lr: 1.0000e-05 - 312ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8015 - val_loss: 0.0643 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8011 - val_loss: 0.0644 - lr: 1.0000e-05 - 303ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8008 - val_loss: 0.0644 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8004 - val_loss: 0.0645 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.8001 - val_loss: 0.0646 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7997 - val_loss: 0.0647 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7994 - val_loss: 0.0648 - lr: 1.0000e-05 - 308ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7990 - val_loss: 0.0648 - lr: 1.0000e-05 - 318ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7987 - val_loss: 0.0649 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7983 - val_loss: 0.0650 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7979 - val_loss: 0.0651 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7976 - val_loss: 0.0652 - lr: 1.0000e-05 - 342ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7972 - val_loss: 0.0653 - lr: 1.0000e-05 - 316ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7968 - val_loss: 0.0654 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7965 - val_loss: 0.0655 - lr: 1.0000e-05 - 308ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7961 - val_loss: 0.0656 - lr: 1.0000e-05 - 300ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7957 - val_loss: 0.0657 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7953 - val_loss: 0.0658 - lr: 1.0000e-05 - 294ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7950 - val_loss: 0.0659 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7946 - val_loss: 0.0660 - lr: 1.0000e-05 - 333ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7942 - val_loss: 0.0661 - lr: 1.0000e-05 - 315ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7938 - val_loss: 0.0662 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7935 - val_loss: 0.0663 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7931 - val_loss: 0.0664 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7927 - val_loss: 0.0665 - lr: 1.0000e-05 - 332ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7923 - val_loss: 0.0666 - lr: 1.0000e-05 - 344ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7920 - val_loss: 0.0667 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7916 - val_loss: 0.0668 - lr: 1.0000e-05 - 322ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7912 - val_loss: 0.0669 - lr: 1.0000e-05 - 312ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7908 - val_loss: 0.0670 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7905 - val_loss: 0.0671 - lr: 1.0000e-05 - 317ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7901 - val_loss: 0.0672 - lr: 1.0000e-05 - 326ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7897 - val_loss: 0.0674 - lr: 1.0000e-05 - 306ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7893 - val_loss: 0.0675 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7890 - val_loss: 0.0676 - lr: 1.0000e-05 - 312ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7886 - val_loss: 0.0677 - lr: 1.0000e-05 - 329ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04095
58/58 - 0s - loss: 0.7882 - val_loss: 0.0678 - lr: 1.0000e-05 - 313ms/epoch - 5ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 52.4753296205182 
RMSE:	 7.2439857551294375 
MAPE:	 5.852253139584933

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 146.44755629127866 
RMSE:	 12.10155181335347 
MAPE:	 10.943210296434415

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 19.64215945229788 
RMSE:	 4.4319475913302355 
MAPE:	 3.5686191181651687

MIDPOINT
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 19.83404242536117 
RMSE:	 4.453542682557468 
MAPE:	 3.5743844299716057
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.35 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4414.515, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3944.062, Time=0.04 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.36 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3715.173, Time=0.06 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3577.471, Time=0.09 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.39 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.60 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3579.471, Time=0.19 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.115 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1784.736
Date:                Sun, 12 Dec 2021   AIC                           3577.471
Time:                        15:26:44   BIC                           3596.235
Sample:                             0   HQIC                          3584.677
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1982      0.003   -389.844      0.000      -1.204      -1.192
ar.L2         -0.8974      0.006   -139.861      0.000      -0.910      -0.885
ar.L3         -0.3983      0.006    -68.862      0.000      -0.410      -0.387
sigma2         4.9242      0.023    215.469      0.000       4.879       4.969
===================================================================================
Ljung-Box (L1) (Q):                  14.55   Jarque-Bera (JB):           2468024.38
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             3.90
Prob(H) (two-sided):                  0.00   Kurtosis:                       274.15
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04951, saving model to LSTM4.h5
43/43 - 4s - loss: 1.4093 - val_loss: 0.0495 - lr: 0.0010 - 4s/epoch - 83ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.3586 - val_loss: 0.0516 - lr: 0.0010 - 248ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.3003 - val_loss: 0.0540 - lr: 0.0010 - 227ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.2446 - val_loss: 0.0567 - lr: 0.0010 - 247ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.1921 - val_loss: 0.0597 - lr: 0.0010 - 240ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.1433 - val_loss: 0.0629 - lr: 0.0010 - 226ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.1152 - val_loss: 0.0632 - lr: 1.0000e-04 - 238ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.1110 - val_loss: 0.0636 - lr: 1.0000e-04 - 264ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.1069 - val_loss: 0.0639 - lr: 1.0000e-04 - 262ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.1029 - val_loss: 0.0643 - lr: 1.0000e-04 - 250ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0989 - val_loss: 0.0646 - lr: 1.0000e-04 - 223ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0964 - val_loss: 0.0647 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0960 - val_loss: 0.0647 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0956 - val_loss: 0.0647 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0952 - val_loss: 0.0648 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0948 - val_loss: 0.0648 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0944 - val_loss: 0.0649 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0940 - val_loss: 0.0649 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0936 - val_loss: 0.0649 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0932 - val_loss: 0.0650 - lr: 1.0000e-05 - 245ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0928 - val_loss: 0.0650 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0924 - val_loss: 0.0651 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0920 - val_loss: 0.0651 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0916 - val_loss: 0.0651 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0912 - val_loss: 0.0652 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0908 - val_loss: 0.0652 - lr: 1.0000e-05 - 283ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0904 - val_loss: 0.0653 - lr: 1.0000e-05 - 295ms/epoch - 7ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0900 - val_loss: 0.0653 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0896 - val_loss: 0.0654 - lr: 1.0000e-05 - 304ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0892 - val_loss: 0.0654 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0888 - val_loss: 0.0654 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0885 - val_loss: 0.0655 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0881 - val_loss: 0.0655 - lr: 1.0000e-05 - 245ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0877 - val_loss: 0.0656 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0873 - val_loss: 0.0656 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0869 - val_loss: 0.0657 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0865 - val_loss: 0.0657 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0861 - val_loss: 0.0658 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0857 - val_loss: 0.0658 - lr: 1.0000e-05 - 242ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0853 - val_loss: 0.0659 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0849 - val_loss: 0.0659 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0845 - val_loss: 0.0659 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0841 - val_loss: 0.0660 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0837 - val_loss: 0.0660 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0833 - val_loss: 0.0661 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0829 - val_loss: 0.0661 - lr: 1.0000e-05 - 281ms/epoch - 7ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0825 - val_loss: 0.0662 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0821 - val_loss: 0.0662 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0818 - val_loss: 0.0663 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0814 - val_loss: 0.0663 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04951
43/43 - 0s - loss: 1.0810 - val_loss: 0.0664 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 52.4753296205182 
RMSE:	 7.2439857551294375 
MAPE:	 5.852253139584933

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 146.44755629127866 
RMSE:	 12.10155181335347 
MAPE:	 10.943210296434415

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 19.64215945229788 
RMSE:	 4.4319475913302355 
MAPE:	 3.5686191181651687

MIDPOINT
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 19.83404242536117 
RMSE:	 4.453542682557468 
MAPE:	 3.5743844299716057

T3
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 70.66866288490243 
RMSE:	 8.406465540576637 
MAPE:	 6.802843731006552
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=inf, Time=0.47 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=4352.703, Time=0.03 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=3889.412, Time=0.05 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=inf, Time=0.25 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=3689.930, Time=0.05 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=3574.245, Time=0.08 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=inf, Time=1.18 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=inf, Time=0.79 sec
 ARIMA(3,3,0)(0,0,0)[0] intercept   : AIC=3576.245, Time=0.18 sec

Best model:  ARIMA(3,3,0)(0,0,0)[0]          
Total fit time: 3.098 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 0)   Log Likelihood               -1783.123
Date:                Sun, 12 Dec 2021   AIC                           3574.245
Time:                        15:28:06   BIC                           3593.008
Sample:                             0   HQIC                          3581.451
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1480      0.004   -302.430      0.000      -1.155      -1.141
ar.L2         -0.8300      0.008    -99.682      0.000      -0.846      -0.814
ar.L3         -0.3687      0.007    -50.527      0.000      -0.383      -0.354
sigma2         4.9055      0.028    175.970      0.000       4.851       4.960
===================================================================================
Ljung-Box (L1) (Q):                  11.61   Jarque-Bera (JB):           1261976.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.16   Skew:                             2.52
Prob(H) (two-sided):                  0.00   Kurtosis:                       196.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
ARIMA order: (3, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04045, saving model to LSTM4.h5
90/90 - 4s - loss: 1.2159 - val_loss: 0.0404 - lr: 0.0010 - 4s/epoch - 47ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.9800 - val_loss: 0.0482 - lr: 0.0010 - 524ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.8617 - val_loss: 0.0639 - lr: 0.0010 - 443ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.8000 - val_loss: 0.0842 - lr: 0.0010 - 467ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.7583 - val_loss: 0.1038 - lr: 0.0010 - 502ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.7272 - val_loss: 0.1197 - lr: 0.0010 - 462ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.7111 - val_loss: 0.1210 - lr: 1.0000e-04 - 534ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.7087 - val_loss: 0.1224 - lr: 1.0000e-04 - 533ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.7062 - val_loss: 0.1238 - lr: 1.0000e-04 - 443ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.7037 - val_loss: 0.1251 - lr: 1.0000e-04 - 482ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.7012 - val_loss: 0.1265 - lr: 1.0000e-04 - 447ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6996 - val_loss: 0.1266 - lr: 1.0000e-05 - 451ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6993 - val_loss: 0.1268 - lr: 1.0000e-05 - 479ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6991 - val_loss: 0.1269 - lr: 1.0000e-05 - 437ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6988 - val_loss: 0.1271 - lr: 1.0000e-05 - 447ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6985 - val_loss: 0.1272 - lr: 1.0000e-05 - 584ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6982 - val_loss: 0.1274 - lr: 1.0000e-05 - 449ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6979 - val_loss: 0.1276 - lr: 1.0000e-05 - 440ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6976 - val_loss: 0.1277 - lr: 1.0000e-05 - 548ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6974 - val_loss: 0.1279 - lr: 1.0000e-05 - 516ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6971 - val_loss: 0.1281 - lr: 1.0000e-05 - 504ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6967 - val_loss: 0.1282 - lr: 1.0000e-05 - 515ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6964 - val_loss: 0.1284 - lr: 1.0000e-05 - 457ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6961 - val_loss: 0.1286 - lr: 1.0000e-05 - 534ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6958 - val_loss: 0.1288 - lr: 1.0000e-05 - 475ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6955 - val_loss: 0.1290 - lr: 1.0000e-05 - 459ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6952 - val_loss: 0.1291 - lr: 1.0000e-05 - 488ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6949 - val_loss: 0.1293 - lr: 1.0000e-05 - 480ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6946 - val_loss: 0.1295 - lr: 1.0000e-05 - 568ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6942 - val_loss: 0.1297 - lr: 1.0000e-05 - 560ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6939 - val_loss: 0.1299 - lr: 1.0000e-05 - 439ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6936 - val_loss: 0.1301 - lr: 1.0000e-05 - 527ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6933 - val_loss: 0.1303 - lr: 1.0000e-05 - 483ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6929 - val_loss: 0.1305 - lr: 1.0000e-05 - 445ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6926 - val_loss: 0.1307 - lr: 1.0000e-05 - 471ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6923 - val_loss: 0.1309 - lr: 1.0000e-05 - 427ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6920 - val_loss: 0.1311 - lr: 1.0000e-05 - 481ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6916 - val_loss: 0.1313 - lr: 1.0000e-05 - 415ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6913 - val_loss: 0.1315 - lr: 1.0000e-05 - 481ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6910 - val_loss: 0.1317 - lr: 1.0000e-05 - 424ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6907 - val_loss: 0.1319 - lr: 1.0000e-05 - 533ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6903 - val_loss: 0.1321 - lr: 1.0000e-05 - 433ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6900 - val_loss: 0.1323 - lr: 1.0000e-05 - 525ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6897 - val_loss: 0.1324 - lr: 1.0000e-05 - 467ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6893 - val_loss: 0.1326 - lr: 1.0000e-05 - 469ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6890 - val_loss: 0.1328 - lr: 1.0000e-05 - 442ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6887 - val_loss: 0.1330 - lr: 1.0000e-05 - 520ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6883 - val_loss: 0.1332 - lr: 1.0000e-05 - 460ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6880 - val_loss: 0.1334 - lr: 1.0000e-05 - 461ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04045
90/90 - 0s - loss: 0.6877 - val_loss: 0.1336 - lr: 1.0000e-05 - 421ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04045
90/90 - 1s - loss: 0.6874 - val_loss: 0.1338 - lr: 1.0000e-05 - 540ms/epoch - 6ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 19.776724587061057 
RMSE:	 4.447102943159856 
MAPE:	 3.587879520041786

EMA
Prediction vs Close:		57.09% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 31.621751516368622 
RMSE:	 5.623322106759368 
MAPE:	 4.355106062590965

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 52.4753296205182 
RMSE:	 7.2439857551294375 
MAPE:	 5.852253139584933

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 146.44755629127866 
RMSE:	 12.10155181335347 
MAPE:	 10.943210296434415

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 19.64215945229788 
RMSE:	 4.4319475913302355 
MAPE:	 3.5686191181651687

MIDPOINT
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	45.52% Accuracy
MSE:	 19.83404242536117 
RMSE:	 4.453542682557468 
MAPE:	 3.5743844299716057

T3
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 70.66866288490243 
RMSE:	 8.406465540576637 
MAPE:	 6.802843731006552

TEMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	48.88% Accuracy
MSE:	 14.860699364166678 
RMSE:	 3.8549577642519868 
MAPE:	 3.1502795604602833
Runtime: mins: 11.725093292033337

Architecture Used

In [99]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
In [100]:
img = cv2.imread('Experiment4.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[100]:
<matplotlib.image.AxesImage at 0x7fa5ea202a50>

Model Plots

In [101]:
for i in range(len(list(simulation4.keys()))):
  SIM = list(simulation4.keys())[i]
  plot_train(simulation4,SIM)
  plot_test(simulation4,SIM)
----- Train RMSE for SMA ----- 2.7003156237977874
----- Train_MSE_LSTM for SMA ----- 7.291704468126434
----- Train MAE LSTM for SMA ----- 2.660792327163243
----- Test RMSE for SMA----- 4.447102943159856
----- Test_MSE_LSTM for SMA----- 19.776724587061057
----- Test_MAE_LSTM for SMA----- 3.587879520041786
----- Train RMSE for EMA ----- 1.713593446317321
----- Train_MSE_LSTM for EMA ----- 2.9364024992616735
----- Train MAE LSTM for EMA ----- 1.5223039660123314
----- Test RMSE for EMA----- 5.623322106759368
----- Test_MSE_LSTM for EMA----- 31.621751516368622
----- Test_MAE_LSTM for EMA----- 4.355106062590965
----- Train RMSE for WMA ----- 3.927450398854089
----- Train_MSE_LSTM for WMA ----- 15.424866635459145
----- Train MAE LSTM for WMA ----- 3.865711769255081
----- Test RMSE for WMA----- 7.2439857551294375
----- Test_MSE_LSTM for WMA----- 52.4753296205182
----- Test_MAE_LSTM for WMA----- 5.852253139584933
----- Train RMSE for DEMA ----- 2.0481672182170936
----- Train_MSE_LSTM for DEMA ----- 4.194988953779147
----- Train MAE LSTM for DEMA ----- 1.7103755544907977
----- Test RMSE for DEMA----- 12.10155181335347
----- Test_MSE_LSTM for DEMA----- 146.44755629127866
----- Test_MAE_LSTM for DEMA----- 10.943210296434415
----- Train RMSE for KAMA ----- 4.06602280300264
----- Train_MSE_LSTM for KAMA ----- 16.532541434537443
----- Train MAE LSTM for KAMA ----- 4.004387627733816
----- Test RMSE for KAMA----- 4.4319475913302355
----- Test_MSE_LSTM for KAMA----- 19.64215945229788
----- Test_MAE_LSTM for KAMA----- 3.5686191181651687
----- Train RMSE for MIDPOINT ----- 3.3807826675549606
----- Train_MSE_LSTM for MIDPOINT ----- 11.429691445240035
----- Train MAE LSTM for MIDPOINT ----- 3.34850282244163
----- Test RMSE for MIDPOINT----- 4.453542682557468
----- Test_MSE_LSTM for MIDPOINT----- 19.83404242536117
----- Test_MAE_LSTM for MIDPOINT----- 3.5743844299716057
----- Train RMSE for T3 ----- 3.7130600011370944
----- Train_MSE_LSTM for T3 ----- 13.786814572044198
----- Train MAE LSTM for T3 ----- 3.666137409682321
----- Test RMSE for T3----- 8.406465540576637
----- Test_MSE_LSTM for T3----- 70.66866288490243
----- Test_MAE_LSTM for T3----- 6.802843731006552
----- Train RMSE for TEMA ----- 1.286632285419522
----- Train_MSE_LSTM for TEMA ----- 1.6554226378838626
----- Train MAE LSTM for TEMA ----- 1.1888332532183958
----- Test RMSE for TEMA----- 3.8549577642519868
----- Test_MSE_LSTM for TEMA----- 14.860699364166678
----- Test_MAE_LSTM for TEMA----- 3.1502795604602833

Arima w Exogenous Variable Multistep MutiVariate LSTM Hybrid Model Experiment 5

In [112]:
def get_arima_exog(dataframe,original_data, train_len, test_len):    
    

    # prepare train and test data for exogenous vr
    X_value = pd.DataFrame(low_vol.iloc[:, :])
    y_value = pd.DataFrame(low_vol.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    X_scale_dataset = X_scaler.fit_transform(X_value)
    y_scale_dataset = y_scaler.fit_transform(y_value)
    # Get data and check shape
    # X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X_scale_dataset)
    y_train, y_test, = split_train_test(y_scale_dataset)
    yc_train,yc_test = split_train_test(low_vol_data)
    yc = yc_test.values.tolist()
    y_train_list = y_train.flatten().tolist()
    y_test_list = y_test.flatten().tolist()
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)

    # Initialize model
    model = auto_arima(y_train_list,exogenous  = X_train,trace=True, error_action='ignore', start_p=1,start_q=1,max_p=3,max_q=3,d=3,
            suppress_warnings=True,stepwise=True,seasonal=True)

      # Determine model parameters
    print(model.summary())
    model.fit(y_train_list,maxiter=200)
    order = model.get_params()['order']
    print('ARIMA order:', order, '\n')

      # Genereate predictions
    prediction = []
    for i in range(len(y_test_list)):
        model = pmdarima.ARIMA(order=order)
        model.fit(y_train_list)
        # print('working on', i+1, 'of', len(y_test), '-- ' + str(int(100 * (i + 1) / len(y_test))) + '% complete')

        prediction.append(model.predict()[0])
        y_train_list.append(y_test_list[i])

    predictionte = y_scaler.inverse_transform(np.array(prediction).reshape(-1,1))
    y_test_ = y_scaler.inverse_transform(np.array(y_test_list).reshape(-1,1))

    # Generate error data
    mse = mean_squared_error(yc_test, predictionte)
    rmse = mse ** 0.5
    mae = mean_absolute_error(y_test_ , predictionte )
    return yc,predictionte.flatten().tolist(), mse, rmse, mae
In [113]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det = 20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # Option 1
    # Set up & fit LSTM RNN
    model = Sequential()
    model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    model.add(Dense(units=64,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(units=output_dim))
    model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    ## Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM5.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()


    # # option 2
    # model = Sequential()
    # model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    # model.add(Dense(64))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    # class Double_Tanh(Activation):
    #     def __init__(self, activation, **kwargs):
    #         super(Double_Tanh, self).__init__(activation, **kwargs)
    #         self.__name__ = 'double_tanh'

    # def double_tanh(x):
    #     return (K.tanh(x) * 2)

    # get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
    #     # Model Generation
    # model = Sequential()
    # #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    # model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    # model.add(Dense(1))
    # model.add(Activation(double_tanh))
    # model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(input_dim, feature_size)))
    # model.add(LSTM(units=int(lstm_len/2)))
    # model.add(Dense(1, activation='sigmoid'))
    # model.compile(loss='mean_squared_error', optimizer='adam')
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM5.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [114]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation5 = {}
    imgfile = 'Experiment5'
    for ma in optimized_period:
                print(ma)
                print(functions[ma])
                print ( int( optimized_period[ma]))
              # if ma == 'SMA':
                low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
                low_vol = low_vol.fillna(0)
                low_vol_data = df['close']
                high_vol = pd.DataFrame()
                df2 = df.copy()
                for i in df2.columns:
                  if i in low_vol.columns:
                    high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
                high_vol_data = df['close']
                ## *****************************************************
                # Generate ARIMA and LSTM predictions
                print('\nWorking on ' + ma + ' predictions')
                try:
                  print('parameters used : ', train_len, test_len)
                  low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima_exog(low_vol,low_vol_data, train_len, test_len)
                except:
                    print('ARIMA error, skipping to next MA type')
                    continue
                Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
                final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
                mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
                rmse_ftr = mse_ftr ** 0.5
                mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
                mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

                final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
                mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
                rmse = mse ** 0.5
                mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                # Generate prediction accuracy
                actual = df['close'].tail(test_len).values
                result_1 = []
                result_2 = []
                for i in range(1, len(final_prediction)):
                    # Compare prediction to previous close price
                    if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                        result_1.append(1)
                    elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                        result_1.append(1)
                    else:
                        result_1.append(0)

                    # Compare prediction to previous prediction
                    if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                        result_2.append(1)
                    elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                        result_2.append(1)
                    else:
                        result_2.append(0)

                accuracy_1 = np.mean(result_1)
                accuracy_2 = np.mean(result_2)

                simulation5[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                              'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                  'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                              'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                  'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                              'rmse': rmse_ftr, 'mae' : mae_ftr},
                                  'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                            'rmse': rmse, 'mae': mae },
                                  'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

                # save simulation data here as checkpoint
                with open('simulation5_data.json', 'w') as fp:
                    json.dump(simulation5, fp)

                for ma in simulation5.keys():
                    print('\n' + ma)
                    print('Prediction vs Close:\t\t' + str(round(100*simulation5[ma]['accuracy']['prediction vs close'], 2))
                          + '% Accuracy')
                    print('Prediction vs Prediction:\t' + str(round(100*simulation5[ma]['accuracy']['prediction vs prediction'], 2))
                          + '% Accuracy')
                    print('MSE:\t', simulation5[ma]['final']['mse'],
                          '\nRMSE:\t', simulation5[ma]['final']['rmse'],
                          '\nMAPE:\t', simulation5[ma]['final']['mae'])#,
                          # '\nMAPE:\t', simulation[ma]['final']['mape'])
              # else:
              #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.787, Time=3.85 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.588, Time=5.46 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14596.280, Time=5.73 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.588, Time=8.46 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16924.805, Time=10.67 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14482.349, Time=10.95 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17215.608, Time=21.20 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.588, Time=10.42 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15570.350, Time=19.26 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11671.292, Time=27.08 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 123.097 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8639.804
Date:                Sun, 12 Dec 2021   AIC                         -17215.608
Time:                        16:07:33   BIC                         -17065.501
Sample:                             0   HQIC                        -17157.961
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -4.057e-09   5.82e-05  -6.97e-05      1.000      -0.000       0.000
x2         -4.057e-09   5.81e-05  -6.99e-05      1.000      -0.000       0.000
x3         -4.111e-09   5.49e-05  -7.49e-05      1.000      -0.000       0.000
x4             1.0000   5.71e-05   1.75e+04      0.000       1.000       1.000
x5         -3.706e-09   5.43e-05  -6.82e-05      1.000      -0.000       0.000
x6         -1.082e-08      0.000  -6.08e-05      1.000      -0.000       0.000
x7         -4.025e-09   5.63e-05  -7.15e-05      1.000      -0.000       0.000
x8         -4.035e-09   5.19e-05  -7.78e-05      1.000      -0.000       0.000
x9         -1.522e-10    2.9e-05  -5.25e-06      1.000   -5.68e-05    5.68e-05
x10        -6.396e-10   1.04e-05  -6.15e-05      1.000   -2.04e-05    2.04e-05
x11        -3.921e-09   5.06e-05  -7.75e-05      1.000   -9.91e-05    9.91e-05
x12        -4.102e-09   5.29e-05  -7.76e-05      1.000      -0.000       0.000
x13        -4.087e-09   5.75e-05  -7.11e-05      1.000      -0.000       0.000
x14        -3.619e-08      0.000     -0.000      1.000      -0.000       0.000
x15        -4.806e-09   4.61e-05     -0.000      1.000   -9.03e-05    9.03e-05
x16        -3.507e-09      0.000  -2.98e-05      1.000      -0.000       0.000
x17        -3.121e-09   6.02e-05  -5.18e-05      1.000      -0.000       0.000
x18        -1.172e-08      0.000     -0.000      1.000      -0.000       0.000
x19        -5.433e-09   6.06e-05  -8.96e-05      1.000      -0.000       0.000
x20        -1.393e-08   4.79e-05     -0.000      1.000   -9.39e-05    9.39e-05
x21        -4.216e-09   6.63e-05  -6.36e-05      1.000      -0.000       0.000
x22        -3.479e-11   1.66e-08     -0.002      0.998   -3.25e-08    3.24e-08
x23        -9.221e-10    1.4e-07     -0.007      0.995   -2.74e-07    2.73e-07
x24        -8.085e-08      0.001  -6.96e-05      1.000      -0.002       0.002
x25        -9.642e-08      0.001     -0.000      1.000      -0.002       0.002
x26        -5.019e-08      0.000     -0.000      1.000      -0.000       0.000
x27        -2.457e-08   7.65e-05     -0.000      1.000      -0.000       0.000
x28        -3.411e-08      0.000     -0.000      1.000      -0.000       0.000
x29        -1.507e-08   4.36e-05     -0.000      1.000   -8.54e-05    8.54e-05
ma.L1         -1.3898   8.03e-07  -1.73e+06      0.000      -1.390      -1.390
ma.L2          0.4031   8.36e-07   4.82e+05      0.000       0.403       0.403
sigma2      7.528e-11   7.24e-11      1.040      0.298   -6.66e-11    2.17e-10
===================================================================================
Ljung-Box (L1) (Q):                  89.12   Jarque-Bera (JB):           1533103.33
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             5.56
Prob(H) (two-sided):                  0.00   Kurtosis:                       216.50
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.08e+25. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

WARNING:tensorflow:Layer lstm_57 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_57 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.25612, saving model to LSTM5.h5
48/48 - 2s - loss: 0.4353 - val_loss: 0.2561 - lr: 0.0010 - 2s/epoch - 39ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.25612 to 0.11825, saving model to LSTM5.h5
48/48 - 0s - loss: 0.1198 - val_loss: 0.1182 - lr: 0.0010 - 456ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.11825 to 0.11433, saving model to LSTM5.h5
48/48 - 0s - loss: 0.0779 - val_loss: 0.1143 - lr: 0.0010 - 432ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.11433
48/48 - 0s - loss: 0.0580 - val_loss: 0.1983 - lr: 0.0010 - 402ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.11433 to 0.02870, saving model to LSTM5.h5
48/48 - 0s - loss: 0.0636 - val_loss: 0.0287 - lr: 0.0010 - 499ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0631 - val_loss: 0.5273 - lr: 0.0010 - 422ms/epoch - 9ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0476 - val_loss: 0.2411 - lr: 0.0010 - 411ms/epoch - 9ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0466 - val_loss: 0.1020 - lr: 0.0010 - 412ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0515 - val_loss: 0.4444 - lr: 0.0010 - 408ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00010: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0405 - val_loss: 0.2259 - lr: 0.0010 - 417ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0415 - val_loss: 0.2207 - lr: 1.0000e-04 - 379ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0369 - val_loss: 0.2140 - lr: 1.0000e-04 - 466ms/epoch - 10ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0336 - val_loss: 0.2095 - lr: 1.0000e-04 - 387ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0392 - val_loss: 0.2049 - lr: 1.0000e-04 - 417ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00015: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0362 - val_loss: 0.1977 - lr: 1.0000e-04 - 406ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0357 - val_loss: 0.1975 - lr: 1.0000e-05 - 377ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0379 - val_loss: 0.1971 - lr: 1.0000e-05 - 407ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0373 - val_loss: 0.1969 - lr: 1.0000e-05 - 409ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0379 - val_loss: 0.1965 - lr: 1.0000e-05 - 446ms/epoch - 9ms/step
Epoch 20/500

Epoch 00020: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00020: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0361 - val_loss: 0.1961 - lr: 1.0000e-05 - 406ms/epoch - 8ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0378 - val_loss: 0.1957 - lr: 1.0000e-05 - 415ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0350 - val_loss: 0.1953 - lr: 1.0000e-05 - 437ms/epoch - 9ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0357 - val_loss: 0.1945 - lr: 1.0000e-05 - 426ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0379 - val_loss: 0.1938 - lr: 1.0000e-05 - 401ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0388 - val_loss: 0.1927 - lr: 1.0000e-05 - 378ms/epoch - 8ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0341 - val_loss: 0.1916 - lr: 1.0000e-05 - 385ms/epoch - 8ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0353 - val_loss: 0.1913 - lr: 1.0000e-05 - 393ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0389 - val_loss: 0.1907 - lr: 1.0000e-05 - 400ms/epoch - 8ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0335 - val_loss: 0.1898 - lr: 1.0000e-05 - 405ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0348 - val_loss: 0.1892 - lr: 1.0000e-05 - 442ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0362 - val_loss: 0.1879 - lr: 1.0000e-05 - 386ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0377 - val_loss: 0.1861 - lr: 1.0000e-05 - 372ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0366 - val_loss: 0.1856 - lr: 1.0000e-05 - 421ms/epoch - 9ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0359 - val_loss: 0.1856 - lr: 1.0000e-05 - 417ms/epoch - 9ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0345 - val_loss: 0.1849 - lr: 1.0000e-05 - 390ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0315 - val_loss: 0.1843 - lr: 1.0000e-05 - 405ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0351 - val_loss: 0.1835 - lr: 1.0000e-05 - 379ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0342 - val_loss: 0.1831 - lr: 1.0000e-05 - 407ms/epoch - 8ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0343 - val_loss: 0.1819 - lr: 1.0000e-05 - 481ms/epoch - 10ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0327 - val_loss: 0.1816 - lr: 1.0000e-05 - 379ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0316 - val_loss: 0.1806 - lr: 1.0000e-05 - 417ms/epoch - 9ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0349 - val_loss: 0.1790 - lr: 1.0000e-05 - 432ms/epoch - 9ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0354 - val_loss: 0.1780 - lr: 1.0000e-05 - 416ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0307 - val_loss: 0.1776 - lr: 1.0000e-05 - 458ms/epoch - 10ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0325 - val_loss: 0.1778 - lr: 1.0000e-05 - 490ms/epoch - 10ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0364 - val_loss: 0.1772 - lr: 1.0000e-05 - 398ms/epoch - 8ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0339 - val_loss: 0.1769 - lr: 1.0000e-05 - 464ms/epoch - 10ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0331 - val_loss: 0.1767 - lr: 1.0000e-05 - 430ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0326 - val_loss: 0.1763 - lr: 1.0000e-05 - 376ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0328 - val_loss: 0.1755 - lr: 1.0000e-05 - 381ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0352 - val_loss: 0.1756 - lr: 1.0000e-05 - 411ms/epoch - 9ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0312 - val_loss: 0.1758 - lr: 1.0000e-05 - 372ms/epoch - 8ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0327 - val_loss: 0.1758 - lr: 1.0000e-05 - 398ms/epoch - 8ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0343 - val_loss: 0.1750 - lr: 1.0000e-05 - 421ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.02870
48/48 - 0s - loss: 0.0307 - val_loss: 0.1742 - lr: 1.0000e-05 - 416ms/epoch - 9ms/step
Epoch 00055: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.79 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.47 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15952.568, Time=15.15 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=7.81 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16628.634, Time=10.62 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16462.206, Time=24.29 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16848.298, Time=12.82 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.023, Time=6.57 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.619, Time=3.78 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=7.45 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=18.63 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.994, Time=3.86 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.667, Time=5.19 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 125.475 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        16:13:56   BIC                         -16911.966
Sample:                             0   HQIC                        -17010.204
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -2.316e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x2         -2.309e-10   6.24e-05   -3.7e-06      1.000      -0.000       0.000
x3         -2.325e-10   6.26e-05  -3.71e-06      1.000      -0.000       0.000
x4             1.0000   6.25e-05    1.6e+04      0.000       1.000       1.000
x5         -2.107e-10   5.96e-05  -3.54e-06      1.000      -0.000       0.000
x6         -7.997e-10      0.000  -7.41e-06      1.000      -0.000       0.000
x7         -2.295e-10   6.22e-05  -3.69e-06      1.000      -0.000       0.000
x8         -2.246e-10   6.15e-05  -3.65e-06      1.000      -0.000       0.000
x9         -1.167e-11   1.25e-05  -9.33e-07      1.000   -2.45e-05    2.45e-05
x10        -4.454e-11   2.66e-05  -1.68e-06      1.000   -5.21e-05    5.21e-05
x11        -2.221e-10   6.11e-05  -3.63e-06      1.000      -0.000       0.000
x12        -2.266e-10   6.18e-05  -3.66e-06      1.000      -0.000       0.000
x13        -2.315e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x14        -1.767e-09      0.000  -1.02e-05      1.000      -0.000       0.000
x15         -2.11e-10   5.93e-05  -3.56e-06      1.000      -0.000       0.000
x16        -5.283e-10   9.45e-05  -5.59e-06      1.000      -0.000       0.000
x17        -2.098e-10   6.01e-05  -3.49e-06      1.000      -0.000       0.000
x18         -3.82e-11   2.41e-05  -1.58e-06      1.000   -4.73e-05    4.73e-05
x19        -2.645e-10   6.61e-05     -4e-06      1.000      -0.000       0.000
x20        -2.417e-10   6.21e-05  -3.89e-06      1.000      -0.000       0.000
x21        -4.824e-10   8.83e-05  -5.46e-06      1.000      -0.000       0.000
x22        -3.758e-13   1.19e-11     -0.032      0.975   -2.36e-11    2.29e-11
x23        -1.089e-11   8.42e-11     -0.129      0.897   -1.76e-10    1.54e-10
x24        -2.538e-09      0.000  -1.44e-05      1.000      -0.000       0.000
x25        -2.038e-09      0.000  -1.49e-05      1.000      -0.000       0.000
x26         -3.16e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x27        -2.955e-09      0.000  -1.32e-05      1.000      -0.000       0.000
x28        -1.664e-09      0.000  -9.94e-06      1.000      -0.000       0.000
x29        -1.568e-09      0.000  -9.63e-06      1.000      -0.000       0.000
ar.L1         -0.4923    6.2e-10  -7.94e+08      0.000      -0.492      -0.492
ar.L2         -0.1923    3.6e-10  -5.35e+08      0.000      -0.192      -0.192
ar.L3         -0.0462   1.71e-10  -2.71e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.41e-09  -5.04e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  51.79   Jarque-Bera (JB):           4012066.18
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.44
Prob(H) (two-sided):                  0.00   Kurtosis:                       348.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 5.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

WARNING:tensorflow:Layer lstm_58 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_58 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.35754, saving model to LSTM5.h5
16/16 - 2s - loss: 0.7057 - val_loss: 0.3575 - lr: 0.0010 - 2s/epoch - 95ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.35754 to 0.02747, saving model to LSTM5.h5
16/16 - 0s - loss: 0.2192 - val_loss: 0.0275 - lr: 0.0010 - 160ms/epoch - 10ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.02747
16/16 - 0s - loss: 0.1892 - val_loss: 0.0299 - lr: 0.0010 - 166ms/epoch - 10ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.02747
16/16 - 0s - loss: 0.0957 - val_loss: 0.0364 - lr: 0.0010 - 150ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.02747 to 0.01373, saving model to LSTM5.h5
16/16 - 0s - loss: 0.0562 - val_loss: 0.0137 - lr: 0.0010 - 162ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01373
16/16 - 0s - loss: 0.0545 - val_loss: 0.0143 - lr: 0.0010 - 146ms/epoch - 9ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.01373 to 0.01201, saving model to LSTM5.h5
16/16 - 0s - loss: 0.0451 - val_loss: 0.0120 - lr: 0.0010 - 177ms/epoch - 11ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01201
16/16 - 0s - loss: 0.0470 - val_loss: 0.0320 - lr: 0.0010 - 147ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.01201 to 0.00908, saving model to LSTM5.h5
16/16 - 0s - loss: 0.0439 - val_loss: 0.0091 - lr: 0.0010 - 173ms/epoch - 11ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0421 - val_loss: 0.0262 - lr: 0.0010 - 143ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0416 - val_loss: 0.0110 - lr: 0.0010 - 149ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0365 - val_loss: 0.0158 - lr: 0.0010 - 149ms/epoch - 9ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0391 - val_loss: 0.0105 - lr: 0.0010 - 148ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00014: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0346 - val_loss: 0.0095 - lr: 0.0010 - 143ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0378 - val_loss: 0.0097 - lr: 1.0000e-04 - 146ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0320 - val_loss: 0.0102 - lr: 1.0000e-04 - 145ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0342 - val_loss: 0.0102 - lr: 1.0000e-04 - 157ms/epoch - 10ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0342 - val_loss: 0.0110 - lr: 1.0000e-04 - 138ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00019: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0345 - val_loss: 0.0105 - lr: 1.0000e-04 - 143ms/epoch - 9ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0313 - val_loss: 0.0105 - lr: 1.0000e-05 - 146ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0304 - val_loss: 0.0105 - lr: 1.0000e-05 - 152ms/epoch - 10ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0321 - val_loss: 0.0107 - lr: 1.0000e-05 - 148ms/epoch - 9ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0328 - val_loss: 0.0107 - lr: 1.0000e-05 - 152ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00024: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0327 - val_loss: 0.0107 - lr: 1.0000e-05 - 163ms/epoch - 10ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0313 - val_loss: 0.0107 - lr: 1.0000e-05 - 141ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0352 - val_loss: 0.0107 - lr: 1.0000e-05 - 141ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0320 - val_loss: 0.0107 - lr: 1.0000e-05 - 140ms/epoch - 9ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0335 - val_loss: 0.0107 - lr: 1.0000e-05 - 147ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0324 - val_loss: 0.0106 - lr: 1.0000e-05 - 143ms/epoch - 9ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0315 - val_loss: 0.0106 - lr: 1.0000e-05 - 150ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0324 - val_loss: 0.0106 - lr: 1.0000e-05 - 156ms/epoch - 10ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0337 - val_loss: 0.0106 - lr: 1.0000e-05 - 141ms/epoch - 9ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0308 - val_loss: 0.0104 - lr: 1.0000e-05 - 153ms/epoch - 10ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0324 - val_loss: 0.0104 - lr: 1.0000e-05 - 152ms/epoch - 9ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0318 - val_loss: 0.0104 - lr: 1.0000e-05 - 135ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0299 - val_loss: 0.0104 - lr: 1.0000e-05 - 145ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0321 - val_loss: 0.0104 - lr: 1.0000e-05 - 145ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0326 - val_loss: 0.0105 - lr: 1.0000e-05 - 154ms/epoch - 10ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0305 - val_loss: 0.0105 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0315 - val_loss: 0.0104 - lr: 1.0000e-05 - 153ms/epoch - 10ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0351 - val_loss: 0.0105 - lr: 1.0000e-05 - 156ms/epoch - 10ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0330 - val_loss: 0.0106 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0320 - val_loss: 0.0105 - lr: 1.0000e-05 - 147ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0327 - val_loss: 0.0104 - lr: 1.0000e-05 - 153ms/epoch - 10ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0304 - val_loss: 0.0105 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0328 - val_loss: 0.0106 - lr: 1.0000e-05 - 186ms/epoch - 12ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0320 - val_loss: 0.0106 - lr: 1.0000e-05 - 146ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0289 - val_loss: 0.0106 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0319 - val_loss: 0.0106 - lr: 1.0000e-05 - 138ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0338 - val_loss: 0.0105 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0309 - val_loss: 0.0106 - lr: 1.0000e-05 - 152ms/epoch - 10ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0331 - val_loss: 0.0105 - lr: 1.0000e-05 - 150ms/epoch - 9ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0334 - val_loss: 0.0106 - lr: 1.0000e-05 - 146ms/epoch - 9ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0326 - val_loss: 0.0105 - lr: 1.0000e-05 - 137ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0310 - val_loss: 0.0104 - lr: 1.0000e-05 - 142ms/epoch - 9ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0339 - val_loss: 0.0104 - lr: 1.0000e-05 - 147ms/epoch - 9ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0344 - val_loss: 0.0103 - lr: 1.0000e-05 - 161ms/epoch - 10ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0280 - val_loss: 0.0103 - lr: 1.0000e-05 - 148ms/epoch - 9ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00908
16/16 - 0s - loss: 0.0306 - val_loss: 0.0103 - lr: 1.0000e-05 - 182ms/epoch - 11ms/step
Epoch 00059: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.41 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.42 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14597.576, Time=5.58 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=8.18 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15338.693, Time=11.30 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15153.472, Time=27.61 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17112.658, Time=15.58 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.587, Time=10.56 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15106.216, Time=13.87 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-12251.715, Time=33.99 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 135.507 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8588.329
Date:                Sun, 12 Dec 2021   AIC                         -17112.658
Time:                        16:24:58   BIC                         -16962.551
Sample:                             0   HQIC                        -17055.011
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          -4.53e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x2         -4.512e-09   3.25e-06     -0.001      0.999   -6.38e-06    6.37e-06
x3         -4.538e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x4             1.0000   3.26e-06   3.07e+05      0.000       1.000       1.000
x5         -4.105e-09   3.11e-06     -0.001      0.999    -6.1e-06    6.09e-06
x6         -1.488e-08   5.45e-06     -0.003      0.998   -1.07e-05    1.07e-05
x7         -4.481e-09   3.24e-06     -0.001      0.999   -6.36e-06    6.36e-06
x8         -4.365e-09    3.2e-06     -0.001      0.999   -6.29e-06    6.28e-06
x9         -4.628e-10   8.38e-07     -0.001      1.000   -1.64e-06    1.64e-06
x10        -7.326e-10    1.3e-06     -0.001      1.000   -2.55e-06    2.54e-06
x11        -4.347e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x12        -4.345e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x13         -4.52e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x14        -3.586e-08      9e-06     -0.004      0.997   -1.77e-05    1.76e-05
x15        -3.757e-09   2.98e-06     -0.001      0.999   -5.84e-06    5.83e-06
x16         -1.24e-08   5.36e-06     -0.002      0.998   -1.05e-05    1.05e-05
x17        -4.515e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x18        -2.632e-10   7.07e-07     -0.000      1.000   -1.39e-06    1.39e-06
x19        -4.642e-09    3.3e-06     -0.001      0.999   -6.47e-06    6.46e-06
x20        -3.919e-10   6.91e-07     -0.001      1.000   -1.36e-06    1.35e-06
x21         -7.69e-09   4.13e-06     -0.002      0.999   -8.11e-06    8.09e-06
x22        -6.998e-12   2.69e-13    -25.970      0.000   -7.53e-12   -6.47e-12
x23         -1.81e-10   2.22e-12    -81.582      0.000   -1.85e-10   -1.77e-10
x24        -4.955e-08    8.9e-06     -0.006      0.996   -1.75e-05    1.74e-05
x25        -4.901e-08    8.4e-06     -0.006      0.995   -1.65e-05    1.64e-05
x26        -6.446e-08    1.2e-05     -0.005      0.996   -2.37e-05    2.35e-05
x27         -5.73e-08   1.14e-05     -0.005      0.996   -2.24e-05    2.23e-05
x28        -2.997e-08   8.22e-06     -0.004      0.997   -1.61e-05    1.61e-05
x29        -3.486e-08   8.89e-06     -0.004      0.997   -1.75e-05    1.74e-05
ma.L1         -1.3902   3.62e-10  -3.84e+09      0.000      -1.390      -1.390
ma.L2          0.4033   3.72e-10   1.08e+09      0.000       0.403       0.403
sigma2      8.541e-11   6.95e-11      1.229      0.219   -5.08e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  66.92   Jarque-Bera (JB):           6039240.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                            12.14
Prob(H) (two-sided):                  0.00   Kurtosis:                       426.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.94e+30. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

WARNING:tensorflow:Layer lstm_59 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_59 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.26413, saving model to LSTM5.h5
17/17 - 2s - loss: 0.3187 - val_loss: 0.2641 - lr: 0.0010 - 2s/epoch - 113ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.26413 to 0.12648, saving model to LSTM5.h5
17/17 - 0s - loss: 0.1236 - val_loss: 0.1265 - lr: 0.0010 - 178ms/epoch - 10ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.12648
17/17 - 0s - loss: 0.0648 - val_loss: 0.4408 - lr: 0.0010 - 159ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.12648
17/17 - 0s - loss: 0.0783 - val_loss: 0.2928 - lr: 0.0010 - 165ms/epoch - 10ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.12648
17/17 - 0s - loss: 0.1373 - val_loss: 0.1590 - lr: 0.0010 - 154ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.12648 to 0.11518, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0888 - val_loss: 0.1152 - lr: 0.0010 - 187ms/epoch - 11ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.11518
17/17 - 0s - loss: 0.0925 - val_loss: 0.1173 - lr: 0.0010 - 173ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.11518 to 0.11176, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0441 - val_loss: 0.1118 - lr: 0.0010 - 174ms/epoch - 10ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.11176
17/17 - 0s - loss: 0.0418 - val_loss: 0.1480 - lr: 0.0010 - 168ms/epoch - 10ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.11176
17/17 - 0s - loss: 0.0372 - val_loss: 0.1331 - lr: 0.0010 - 161ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.11176 to 0.10336, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0379 - val_loss: 0.1034 - lr: 0.0010 - 179ms/epoch - 11ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.10336
17/17 - 0s - loss: 0.0350 - val_loss: 0.1238 - lr: 0.0010 - 151ms/epoch - 9ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.10336
17/17 - 0s - loss: 0.0343 - val_loss: 0.1104 - lr: 0.0010 - 167ms/epoch - 10ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.10336 to 0.09685, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0289 - val_loss: 0.0969 - lr: 0.0010 - 185ms/epoch - 11ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.09685
17/17 - 0s - loss: 0.0326 - val_loss: 0.1028 - lr: 0.0010 - 204ms/epoch - 12ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.09685 to 0.08657, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0333 - val_loss: 0.0866 - lr: 0.0010 - 169ms/epoch - 10ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.08657
17/17 - 0s - loss: 0.0408 - val_loss: 0.1019 - lr: 0.0010 - 156ms/epoch - 9ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.08657 to 0.03994, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0359 - val_loss: 0.0399 - lr: 0.0010 - 185ms/epoch - 11ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03994
17/17 - 0s - loss: 0.0352 - val_loss: 0.1347 - lr: 0.0010 - 173ms/epoch - 10ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03994
17/17 - 0s - loss: 0.0370 - val_loss: 0.0539 - lr: 0.0010 - 158ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03994
17/17 - 0s - loss: 0.0348 - val_loss: 0.0440 - lr: 0.0010 - 153ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03994
17/17 - 0s - loss: 0.0326 - val_loss: 0.0673 - lr: 0.0010 - 158ms/epoch - 9ms/step
Epoch 23/500

Epoch 00023: val_loss improved from 0.03994 to 0.03396, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0371 - val_loss: 0.0340 - lr: 0.0010 - 168ms/epoch - 10ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03396
17/17 - 0s - loss: 0.0278 - val_loss: 0.1082 - lr: 0.0010 - 171ms/epoch - 10ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03396
17/17 - 0s - loss: 0.0336 - val_loss: 0.0475 - lr: 0.0010 - 163ms/epoch - 10ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03396
17/17 - 0s - loss: 0.0288 - val_loss: 0.0567 - lr: 0.0010 - 159ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03396
17/17 - 0s - loss: 0.0368 - val_loss: 0.0403 - lr: 0.0010 - 177ms/epoch - 10ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.03396 to 0.03342, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0256 - val_loss: 0.0334 - lr: 0.0010 - 176ms/epoch - 10ms/step
Epoch 29/500

Epoch 00029: val_loss improved from 0.03342 to 0.03152, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0273 - val_loss: 0.0315 - lr: 0.0010 - 171ms/epoch - 10ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03152
17/17 - 0s - loss: 0.0231 - val_loss: 0.0574 - lr: 0.0010 - 173ms/epoch - 10ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03152
17/17 - 0s - loss: 0.0227 - val_loss: 0.0366 - lr: 0.0010 - 151ms/epoch - 9ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03152
17/17 - 0s - loss: 0.0227 - val_loss: 0.0813 - lr: 0.0010 - 181ms/epoch - 11ms/step
Epoch 33/500

Epoch 00033: val_loss improved from 0.03152 to 0.01575, saving model to LSTM5.h5
17/17 - 0s - loss: 0.0217 - val_loss: 0.0157 - lr: 0.0010 - 188ms/epoch - 11ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0255 - val_loss: 0.1116 - lr: 0.0010 - 171ms/epoch - 10ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0231 - val_loss: 0.0222 - lr: 0.0010 - 160ms/epoch - 9ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0224 - val_loss: 0.0222 - lr: 0.0010 - 172ms/epoch - 10ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0257 - val_loss: 0.0214 - lr: 0.0010 - 159ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00038: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0213 - val_loss: 0.0353 - lr: 0.0010 - 174ms/epoch - 10ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0206 - val_loss: 0.0310 - lr: 1.0000e-04 - 151ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0205 - val_loss: 0.0274 - lr: 1.0000e-04 - 172ms/epoch - 10ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0225 - val_loss: 0.0250 - lr: 1.0000e-04 - 155ms/epoch - 9ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0197 - val_loss: 0.0229 - lr: 1.0000e-04 - 174ms/epoch - 10ms/step
Epoch 43/500

Epoch 00043: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00043: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0202 - val_loss: 0.0224 - lr: 1.0000e-04 - 171ms/epoch - 10ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0192 - val_loss: 0.0221 - lr: 1.0000e-05 - 181ms/epoch - 11ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0212 - val_loss: 0.0218 - lr: 1.0000e-05 - 152ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0190 - val_loss: 0.0217 - lr: 1.0000e-05 - 155ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0214 - val_loss: 0.0216 - lr: 1.0000e-05 - 150ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00048: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0214 - val_loss: 0.0212 - lr: 1.0000e-05 - 168ms/epoch - 10ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0183 - val_loss: 0.0210 - lr: 1.0000e-05 - 196ms/epoch - 12ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0201 - val_loss: 0.0207 - lr: 1.0000e-05 - 195ms/epoch - 11ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0210 - val_loss: 0.0207 - lr: 1.0000e-05 - 168ms/epoch - 10ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0207 - val_loss: 0.0206 - lr: 1.0000e-05 - 168ms/epoch - 10ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0205 - val_loss: 0.0204 - lr: 1.0000e-05 - 159ms/epoch - 9ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0197 - val_loss: 0.0201 - lr: 1.0000e-05 - 159ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0202 - val_loss: 0.0197 - lr: 1.0000e-05 - 163ms/epoch - 10ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0206 - val_loss: 0.0196 - lr: 1.0000e-05 - 163ms/epoch - 10ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0201 - val_loss: 0.0196 - lr: 1.0000e-05 - 160ms/epoch - 9ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0194 - val_loss: 0.0197 - lr: 1.0000e-05 - 162ms/epoch - 10ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0210 - val_loss: 0.0198 - lr: 1.0000e-05 - 168ms/epoch - 10ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0195 - val_loss: 0.0196 - lr: 1.0000e-05 - 161ms/epoch - 9ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0200 - val_loss: 0.0194 - lr: 1.0000e-05 - 175ms/epoch - 10ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0206 - val_loss: 0.0196 - lr: 1.0000e-05 - 143ms/epoch - 8ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0194 - val_loss: 0.0193 - lr: 1.0000e-05 - 160ms/epoch - 9ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0202 - val_loss: 0.0191 - lr: 1.0000e-05 - 154ms/epoch - 9ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0212 - val_loss: 0.0190 - lr: 1.0000e-05 - 162ms/epoch - 10ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0204 - val_loss: 0.0189 - lr: 1.0000e-05 - 153ms/epoch - 9ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0199 - val_loss: 0.0188 - lr: 1.0000e-05 - 217ms/epoch - 13ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0218 - val_loss: 0.0189 - lr: 1.0000e-05 - 170ms/epoch - 10ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0217 - val_loss: 0.0187 - lr: 1.0000e-05 - 154ms/epoch - 9ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0209 - val_loss: 0.0187 - lr: 1.0000e-05 - 162ms/epoch - 10ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0207 - val_loss: 0.0186 - lr: 1.0000e-05 - 168ms/epoch - 10ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0211 - val_loss: 0.0188 - lr: 1.0000e-05 - 186ms/epoch - 11ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0190 - val_loss: 0.0189 - lr: 1.0000e-05 - 166ms/epoch - 10ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0190 - val_loss: 0.0186 - lr: 1.0000e-05 - 147ms/epoch - 9ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0203 - val_loss: 0.0186 - lr: 1.0000e-05 - 153ms/epoch - 9ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0207 - val_loss: 0.0186 - lr: 1.0000e-05 - 154ms/epoch - 9ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0213 - val_loss: 0.0185 - lr: 1.0000e-05 - 151ms/epoch - 9ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0209 - val_loss: 0.0184 - lr: 1.0000e-05 - 182ms/epoch - 11ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0204 - val_loss: 0.0184 - lr: 1.0000e-05 - 178ms/epoch - 10ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0193 - val_loss: 0.0184 - lr: 1.0000e-05 - 177ms/epoch - 10ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0206 - val_loss: 0.0184 - lr: 1.0000e-05 - 148ms/epoch - 9ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0200 - val_loss: 0.0185 - lr: 1.0000e-05 - 155ms/epoch - 9ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.01575
17/17 - 0s - loss: 0.0199 - val_loss: 0.0185 - lr: 1.0000e-05 - 178ms/epoch - 10ms/step
Epoch 00083: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731

WMA
Prediction vs Close:		55.97% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 70.35376938042184 
RMSE:	 8.387715385039114 
MAPE:	 6.8547592718484545
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.776, Time=3.27 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.586, Time=5.42 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16271.755, Time=7.27 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.586, Time=8.17 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15152.908, Time=11.03 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14481.105, Time=12.78 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16088.109, Time=22.11 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.021, Time=6.67 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.615, Time=4.21 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=8.04 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=19.68 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.981, Time=4.31 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.666, Time=4.70 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 117.691 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        16:31:21   BIC                         -16911.965
Sample:                             0   HQIC                        -17010.203
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -2.8e-10   6.02e-05  -4.65e-06      1.000      -0.000       0.000
x2         -2.817e-10   6.04e-05  -4.66e-06      1.000      -0.000       0.000
x3         -2.805e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x4             1.0000   6.03e-05   1.66e+04      0.000       1.000       1.000
x5           -2.6e-10    5.8e-05  -4.48e-06      1.000      -0.000       0.000
x6         -1.389e-09      0.000  -1.08e-05      1.000      -0.000       0.000
x7         -2.789e-10   6.01e-05  -4.64e-06      1.000      -0.000       0.000
x8         -2.763e-10   5.99e-05  -4.62e-06      1.000      -0.000       0.000
x9         -2.224e-12    1.6e-06  -1.39e-06      1.000   -3.13e-06    3.13e-06
x10        -1.345e-10   4.12e-05  -3.26e-06      1.000   -8.08e-05    8.08e-05
x11          -2.9e-10   6.12e-05  -4.74e-06      1.000      -0.000       0.000
x12        -2.602e-10   5.82e-05  -4.47e-06      1.000      -0.000       0.000
x13        -2.807e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x14         -1.87e-09      0.000   -1.2e-05      1.000      -0.000       0.000
x15        -2.844e-10   6.05e-05   -4.7e-06      1.000      -0.000       0.000
x16        -7.962e-11    3.2e-05  -2.48e-06      1.000   -6.28e-05    6.28e-05
x17        -2.445e-10   5.61e-05  -4.36e-06      1.000      -0.000       0.000
x18          -6.4e-10   9.15e-05  -6.99e-06      1.000      -0.000       0.000
x19        -2.923e-10   6.14e-05  -4.76e-06      1.000      -0.000       0.000
x20        -4.336e-10   7.41e-05  -5.86e-06      1.000      -0.000       0.000
x21         -4.55e-10    7.5e-05  -6.07e-06      1.000      -0.000       0.000
x22        -3.587e-13   1.42e-11     -0.025      0.980   -2.82e-11    2.75e-11
x23        -1.088e-11   9.56e-11     -0.114      0.909   -1.98e-10    1.76e-10
x24        -2.146e-09      0.000  -1.63e-05      1.000      -0.000       0.000
x25        -1.637e-09      0.000  -1.35e-05      1.000      -0.000       0.000
x26        -3.147e-09      0.000  -1.56e-05      1.000      -0.000       0.000
x27         -2.58e-09      0.000  -1.41e-05      1.000      -0.000       0.000
x28        -2.444e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x29        -1.666e-09      0.000  -1.13e-05      1.000      -0.000       0.000
ar.L1         -0.4923    5.1e-10  -9.65e+08      0.000      -0.492      -0.492
ar.L2         -0.1923   2.96e-10  -6.49e+08      0.000      -0.192      -0.192
ar.L3         -0.0462    1.4e-10  -3.29e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.16e-09  -6.12e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  54.06   Jarque-Bera (JB):           4126495.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.48
Prob(H) (two-sided):                  0.00   Kurtosis:                       353.58
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 2.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

WARNING:tensorflow:Layer lstm_60 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_60 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.08624, saving model to LSTM5.h5
10/10 - 2s - loss: 0.8398 - val_loss: 0.0862 - lr: 0.0010 - 2s/epoch - 155ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.08624 to 0.05008, saving model to LSTM5.h5
10/10 - 0s - loss: 0.3040 - val_loss: 0.0501 - lr: 0.0010 - 127ms/epoch - 13ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.05008 to 0.02501, saving model to LSTM5.h5
10/10 - 0s - loss: 0.0974 - val_loss: 0.0250 - lr: 0.0010 - 125ms/epoch - 12ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0654 - val_loss: 0.0903 - lr: 0.0010 - 112ms/epoch - 11ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0475 - val_loss: 0.0980 - lr: 0.0010 - 112ms/epoch - 11ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0445 - val_loss: 0.0713 - lr: 0.0010 - 119ms/epoch - 12ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0459 - val_loss: 0.0613 - lr: 0.0010 - 102ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00008: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0381 - val_loss: 0.1080 - lr: 0.0010 - 107ms/epoch - 11ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0400 - val_loss: 0.1042 - lr: 1.0000e-04 - 99ms/epoch - 10ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0400 - val_loss: 0.0992 - lr: 1.0000e-04 - 106ms/epoch - 11ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0366 - val_loss: 0.0930 - lr: 1.0000e-04 - 92ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0426 - val_loss: 0.0876 - lr: 1.0000e-04 - 101ms/epoch - 10ms/step
Epoch 13/500

Epoch 00013: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00013: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0376 - val_loss: 0.0844 - lr: 1.0000e-04 - 101ms/epoch - 10ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0377 - val_loss: 0.0841 - lr: 1.0000e-05 - 110ms/epoch - 11ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0362 - val_loss: 0.0837 - lr: 1.0000e-05 - 129ms/epoch - 13ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0406 - val_loss: 0.0834 - lr: 1.0000e-05 - 124ms/epoch - 12ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0393 - val_loss: 0.0833 - lr: 1.0000e-05 - 101ms/epoch - 10ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00018: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0387 - val_loss: 0.0831 - lr: 1.0000e-05 - 116ms/epoch - 12ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0382 - val_loss: 0.0826 - lr: 1.0000e-05 - 133ms/epoch - 13ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0374 - val_loss: 0.0820 - lr: 1.0000e-05 - 102ms/epoch - 10ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0389 - val_loss: 0.0812 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0377 - val_loss: 0.0809 - lr: 1.0000e-05 - 104ms/epoch - 10ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0353 - val_loss: 0.0814 - lr: 1.0000e-05 - 110ms/epoch - 11ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0379 - val_loss: 0.0813 - lr: 1.0000e-05 - 113ms/epoch - 11ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0358 - val_loss: 0.0810 - lr: 1.0000e-05 - 120ms/epoch - 12ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0346 - val_loss: 0.0804 - lr: 1.0000e-05 - 106ms/epoch - 11ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0378 - val_loss: 0.0799 - lr: 1.0000e-05 - 109ms/epoch - 11ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0367 - val_loss: 0.0796 - lr: 1.0000e-05 - 119ms/epoch - 12ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0370 - val_loss: 0.0791 - lr: 1.0000e-05 - 121ms/epoch - 12ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0363 - val_loss: 0.0788 - lr: 1.0000e-05 - 102ms/epoch - 10ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0387 - val_loss: 0.0786 - lr: 1.0000e-05 - 108ms/epoch - 11ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0411 - val_loss: 0.0779 - lr: 1.0000e-05 - 116ms/epoch - 12ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0368 - val_loss: 0.0773 - lr: 1.0000e-05 - 118ms/epoch - 12ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0357 - val_loss: 0.0770 - lr: 1.0000e-05 - 94ms/epoch - 9ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0377 - val_loss: 0.0766 - lr: 1.0000e-05 - 109ms/epoch - 11ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0388 - val_loss: 0.0765 - lr: 1.0000e-05 - 111ms/epoch - 11ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0375 - val_loss: 0.0766 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0378 - val_loss: 0.0769 - lr: 1.0000e-05 - 107ms/epoch - 11ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0361 - val_loss: 0.0769 - lr: 1.0000e-05 - 101ms/epoch - 10ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0362 - val_loss: 0.0769 - lr: 1.0000e-05 - 111ms/epoch - 11ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0367 - val_loss: 0.0770 - lr: 1.0000e-05 - 111ms/epoch - 11ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0366 - val_loss: 0.0768 - lr: 1.0000e-05 - 106ms/epoch - 11ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0347 - val_loss: 0.0767 - lr: 1.0000e-05 - 104ms/epoch - 10ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0386 - val_loss: 0.0772 - lr: 1.0000e-05 - 97ms/epoch - 10ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0355 - val_loss: 0.0774 - lr: 1.0000e-05 - 105ms/epoch - 11ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0377 - val_loss: 0.0772 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0392 - val_loss: 0.0771 - lr: 1.0000e-05 - 106ms/epoch - 11ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0348 - val_loss: 0.0769 - lr: 1.0000e-05 - 99ms/epoch - 10ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0383 - val_loss: 0.0762 - lr: 1.0000e-05 - 111ms/epoch - 11ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0378 - val_loss: 0.0759 - lr: 1.0000e-05 - 114ms/epoch - 11ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0369 - val_loss: 0.0756 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0373 - val_loss: 0.0753 - lr: 1.0000e-05 - 96ms/epoch - 10ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02501
10/10 - 0s - loss: 0.0379 - val_loss: 0.0750 - lr: 1.0000e-05 - 109ms/epoch - 11ms/step
Epoch 00053: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731

WMA
Prediction vs Close:		55.97% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 70.35376938042184 
RMSE:	 8.387715385039114 
MAPE:	 6.8547592718484545

DEMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 70.24761196199488 
RMSE:	 8.381384847505505 
MAPE:	 6.862692730259403
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.104, Time=3.80 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.591, Time=5.35 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16779.655, Time=10.90 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.590, Time=7.94 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16989.430, Time=3.84 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-16990.286, Time=3.82 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.543, Time=3.81 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-16987.154, Time=4.18 sec
 ARIMA(2,3,0)(0,0,0)[0] intercept   : AIC=-16533.935, Time=16.39 sec

Best model:  ARIMA(2,3,0)(0,0,0)[0]          
Total fit time: 60.054 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(2, 3, 0)   Log Likelihood                8527.143
Date:                Sun, 12 Dec 2021   AIC                         -16990.286
Time:                        16:41:16   BIC                         -16840.179
Sample:                             0   HQIC                        -16932.639
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -1.1e-16        nan        nan        nan         nan         nan
x2         -3.811e-16         -0        inf      0.000   -3.81e-16   -3.81e-16
x3          8.776e-16   4.38e-27      2e+11      0.000    8.78e-16    8.78e-16
x4             1.0000   4.36e-27   2.29e+26      0.000       1.000       1.000
x5          6.686e-16   4.14e-27   1.61e+11      0.000    6.69e-16    6.69e-16
x6         -5.238e-17   9.44e-27  -5.55e+09      0.000   -5.24e-17   -5.24e-17
x7         -1.709e-16   4.37e-27  -3.91e+10      0.000   -1.71e-16   -1.71e-16
x8          1.439e-15   4.33e-27   3.32e+11      0.000    1.44e-15    1.44e-15
x9         -2.924e-16   5.73e-28   -5.1e+11      0.000   -2.92e-16   -2.92e-16
x10        -1.028e-16   1.78e-27  -5.76e+10      0.000   -1.03e-16   -1.03e-16
x11        -4.338e-16   4.31e-27  -1.01e+11      0.000   -4.34e-16   -4.34e-16
x12          1.72e-16   4.33e-27   3.97e+10      0.000    1.72e-16    1.72e-16
x13        -3.011e-16   4.36e-27  -6.91e+10      0.000   -3.01e-16   -3.01e-16
x14        -2.611e-16   1.27e-26  -2.06e+10      0.000   -2.61e-16   -2.61e-16
x15          1.53e-14   4.46e-27   3.43e+12      0.000    1.53e-14    1.53e-14
x16        -1.401e-14   5.45e-27  -2.57e+12      0.000    -1.4e-14    -1.4e-14
x17         2.316e-14   4.12e-27   5.62e+12      0.000    2.32e-14    2.32e-14
x18        -3.727e-15   3.71e-27  -1.01e+12      0.000   -3.73e-15   -3.73e-15
x19        -1.361e-14   4.94e-27  -2.75e+12      0.000   -1.36e-14   -1.36e-14
x20        -5.277e-15   6.08e-27  -8.68e+11      0.000   -5.28e-15   -5.28e-15
x21         1.178e-18   3.12e-27   3.77e+08      0.000    1.18e-18    1.18e-18
x22        -8.779e-17   1.74e-29  -5.05e+12      0.000   -8.78e-17   -8.78e-17
x23         3.183e-17   5.91e-29   5.39e+11      0.000    3.18e-17    3.18e-17
x24        -1.683e-16   1.41e-26  -1.19e+10      0.000   -1.68e-16   -1.68e-16
x25         8.988e-17   1.48e-30   6.08e+13      0.000    8.99e-17    8.99e-17
x26         4.435e-17   1.58e-26    2.8e+09      0.000    4.44e-17    4.44e-17
x27         1.538e-16   8.87e-27   1.73e+10      0.000    1.54e-16    1.54e-16
x28         1.635e-16   1.22e-26   1.34e+10      0.000    1.63e-16    1.63e-16
x29         1.474e-16   6.34e-27   2.33e+10      0.000    1.47e-16    1.47e-16
ar.L1         -0.9879   1.21e-22  -8.16e+21      0.000      -0.988      -0.988
ar.L2         -0.4879   1.29e-22  -3.79e+21      0.000      -0.488      -0.488
sigma2          1e-10   6.99e-11      1.432      0.152   -3.69e-11    2.37e-10
===================================================================================
Ljung-Box (L1) (Q):                  57.29   Jarque-Bera (JB):            559955.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.13   Skew:                             0.64
Prob(H) (two-sided):                  0.00   Kurtosis:                       132.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/mlemodel.py:2968: RuntimeWarning: divide by zero encountered in true_divide
  return self.params / self.bse
ARIMA order: (2, 3, 0) 

WARNING:tensorflow:Layer lstm_61 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_61 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.03822, saving model to LSTM5.h5
45/45 - 2s - loss: 0.2308 - val_loss: 0.0382 - lr: 0.0010 - 2s/epoch - 39ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.1602 - val_loss: 0.5377 - lr: 0.0010 - 412ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.1071 - val_loss: 0.4634 - lr: 0.0010 - 405ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0711 - val_loss: 0.2545 - lr: 0.0010 - 390ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0477 - val_loss: 0.0663 - lr: 0.0010 - 419ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0372 - val_loss: 0.0460 - lr: 0.0010 - 353ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0389 - val_loss: 0.0517 - lr: 1.0000e-04 - 408ms/epoch - 9ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0437 - val_loss: 0.0554 - lr: 1.0000e-04 - 395ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0402 - val_loss: 0.0707 - lr: 1.0000e-04 - 402ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0443 - val_loss: 0.0625 - lr: 1.0000e-04 - 363ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0361 - val_loss: 0.0628 - lr: 1.0000e-04 - 353ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0376 - val_loss: 0.0630 - lr: 1.0000e-05 - 410ms/epoch - 9ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0385 - val_loss: 0.0642 - lr: 1.0000e-05 - 414ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0359 - val_loss: 0.0655 - lr: 1.0000e-05 - 404ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0332 - val_loss: 0.0661 - lr: 1.0000e-05 - 420ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0391 - val_loss: 0.0670 - lr: 1.0000e-05 - 365ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0389 - val_loss: 0.0670 - lr: 1.0000e-05 - 394ms/epoch - 9ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0360 - val_loss: 0.0666 - lr: 1.0000e-05 - 428ms/epoch - 10ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0372 - val_loss: 0.0668 - lr: 1.0000e-05 - 351ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0380 - val_loss: 0.0675 - lr: 1.0000e-05 - 414ms/epoch - 9ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0393 - val_loss: 0.0670 - lr: 1.0000e-05 - 422ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0354 - val_loss: 0.0660 - lr: 1.0000e-05 - 428ms/epoch - 10ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0377 - val_loss: 0.0661 - lr: 1.0000e-05 - 429ms/epoch - 10ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0382 - val_loss: 0.0663 - lr: 1.0000e-05 - 398ms/epoch - 9ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0375 - val_loss: 0.0664 - lr: 1.0000e-05 - 384ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0339 - val_loss: 0.0672 - lr: 1.0000e-05 - 359ms/epoch - 8ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0364 - val_loss: 0.0669 - lr: 1.0000e-05 - 411ms/epoch - 9ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0338 - val_loss: 0.0670 - lr: 1.0000e-05 - 353ms/epoch - 8ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0407 - val_loss: 0.0666 - lr: 1.0000e-05 - 377ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0355 - val_loss: 0.0666 - lr: 1.0000e-05 - 485ms/epoch - 11ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0348 - val_loss: 0.0667 - lr: 1.0000e-05 - 434ms/epoch - 10ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0372 - val_loss: 0.0670 - lr: 1.0000e-05 - 429ms/epoch - 10ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0372 - val_loss: 0.0662 - lr: 1.0000e-05 - 368ms/epoch - 8ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0397 - val_loss: 0.0656 - lr: 1.0000e-05 - 379ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0355 - val_loss: 0.0654 - lr: 1.0000e-05 - 437ms/epoch - 10ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0350 - val_loss: 0.0664 - lr: 1.0000e-05 - 383ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0341 - val_loss: 0.0654 - lr: 1.0000e-05 - 427ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0353 - val_loss: 0.0649 - lr: 1.0000e-05 - 386ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0334 - val_loss: 0.0659 - lr: 1.0000e-05 - 355ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0359 - val_loss: 0.0653 - lr: 1.0000e-05 - 396ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0354 - val_loss: 0.0651 - lr: 1.0000e-05 - 480ms/epoch - 11ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0327 - val_loss: 0.0647 - lr: 1.0000e-05 - 406ms/epoch - 9ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0354 - val_loss: 0.0636 - lr: 1.0000e-05 - 427ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0401 - val_loss: 0.0639 - lr: 1.0000e-05 - 411ms/epoch - 9ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0384 - val_loss: 0.0649 - lr: 1.0000e-05 - 365ms/epoch - 8ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0341 - val_loss: 0.0650 - lr: 1.0000e-05 - 356ms/epoch - 8ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0341 - val_loss: 0.0638 - lr: 1.0000e-05 - 391ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0346 - val_loss: 0.0627 - lr: 1.0000e-05 - 395ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0369 - val_loss: 0.0622 - lr: 1.0000e-05 - 398ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0346 - val_loss: 0.0634 - lr: 1.0000e-05 - 382ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03822
45/45 - 0s - loss: 0.0344 - val_loss: 0.0647 - lr: 1.0000e-05 - 384ms/epoch - 9ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731

WMA
Prediction vs Close:		55.97% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 70.35376938042184 
RMSE:	 8.387715385039114 
MAPE:	 6.8547592718484545

DEMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 70.24761196199488 
RMSE:	 8.381384847505505 
MAPE:	 6.862692730259403

KAMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 27.01407660930758 
RMSE:	 5.1975067685677505 
MAPE:	 4.263533603346384
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.238, Time=3.59 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.578, Time=5.37 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16746.296, Time=8.30 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.578, Time=8.25 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16987.591, Time=3.67 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16395.520, Time=12.87 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17063.555, Time=12.29 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.578, Time=10.73 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16082.554, Time=20.06 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-15249.608, Time=18.57 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 103.711 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8563.778
Date:                Sun, 12 Dec 2021   AIC                         -17063.555
Time:                        16:44:49   BIC                         -16913.448
Sample:                             0   HQIC                        -17005.908
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.495e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x2         -1.485e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x3         -1.518e-10      0.000  -1.21e-06      1.000      -0.000       0.000
x4             1.0000      0.000   8075.329      0.000       1.000       1.000
x5         -1.356e-10      0.000  -1.15e-06      1.000      -0.000       0.000
x6         -2.861e-09      0.000  -2.38e-05      1.000      -0.000       0.000
x7         -1.374e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x8         -1.371e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x9         -7.133e-11    7.1e-06  -1.01e-05      1.000   -1.39e-05    1.39e-05
x10         -1.23e-10   4.21e-05  -2.92e-06      1.000   -8.24e-05    8.24e-05
x11        -1.357e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x12        -1.401e-10      0.000  -1.11e-06      1.000      -0.000       0.000
x13        -1.436e-10      0.000  -1.16e-06      1.000      -0.000       0.000
x14        -1.179e-09      0.000  -3.22e-06      1.000      -0.001       0.001
x15        -1.651e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x16        -1.064e-10      0.000  -9.62e-07      1.000      -0.000       0.000
x17        -1.041e-10      0.000  -9.53e-07      1.000      -0.000       0.000
x18        -4.477e-10      0.000  -1.99e-06      1.000      -0.000       0.000
x19        -1.816e-10      0.000  -1.26e-06      1.000      -0.000       0.000
x20         -4.37e-10      0.000  -1.96e-06      1.000      -0.000       0.000
x21        -1.371e-09    9.1e-05  -1.51e-05      1.000      -0.000       0.000
x22        -1.059e-11        nan        nan        nan         nan         nan
x23        -9.902e-11   3.83e-09     -0.026      0.979   -7.61e-09    7.41e-09
x24        -5.521e-09      0.000  -1.34e-05      1.000      -0.001       0.001
x25        -4.621e-09   6.42e-05   -7.2e-05      1.000      -0.000       0.000
x26        -1.587e-09      0.000  -3.73e-06      1.000      -0.001       0.001
x27        -8.504e-10      0.000  -2.79e-06      1.000      -0.001       0.001
x28        -1.122e-09      0.000  -3.14e-06      1.000      -0.001       0.001
x29        -6.091e-10      0.000  -2.45e-06      1.000      -0.000       0.000
ma.L1         -1.3318   7.32e-07  -1.82e+06      0.000      -1.332      -1.332
ma.L2          0.3767   7.56e-07   4.98e+05      0.000       0.377       0.377
sigma2      9.093e-11   6.97e-11      1.304      0.192   -4.57e-11    2.28e-10
===================================================================================
Ljung-Box (L1) (Q):                  76.00   Jarque-Bera (JB):            304933.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.03   Skew:                             1.65
Prob(H) (two-sided):                  0.00   Kurtosis:                        98.29
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.19e+28. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

WARNING:tensorflow:Layer lstm_62 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_62 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.18148, saving model to LSTM5.h5
58/58 - 2s - loss: 0.2766 - val_loss: 0.1815 - lr: 0.0010 - 2s/epoch - 33ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.18148
58/58 - 0s - loss: 0.0961 - val_loss: 0.3811 - lr: 0.0010 - 474ms/epoch - 8ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.18148 to 0.16577, saving model to LSTM5.h5
58/58 - 1s - loss: 0.0762 - val_loss: 0.1658 - lr: 0.0010 - 503ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0861 - val_loss: 0.7714 - lr: 0.0010 - 470ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0546 - val_loss: 0.4224 - lr: 0.0010 - 472ms/epoch - 8ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0428 - val_loss: 0.2851 - lr: 0.0010 - 486ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0373 - val_loss: 0.1855 - lr: 0.0010 - 575ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00008: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0384 - val_loss: 0.3007 - lr: 0.0010 - 529ms/epoch - 9ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0439 - val_loss: 0.2758 - lr: 1.0000e-04 - 493ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0342 - val_loss: 0.2544 - lr: 1.0000e-04 - 464ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0303 - val_loss: 0.2392 - lr: 1.0000e-04 - 478ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0298 - val_loss: 0.2257 - lr: 1.0000e-04 - 503ms/epoch - 9ms/step
Epoch 13/500

Epoch 00013: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00013: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0317 - val_loss: 0.2171 - lr: 1.0000e-04 - 482ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0304 - val_loss: 0.2160 - lr: 1.0000e-05 - 506ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0280 - val_loss: 0.2149 - lr: 1.0000e-05 - 470ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0294 - val_loss: 0.2138 - lr: 1.0000e-05 - 532ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0255 - val_loss: 0.2129 - lr: 1.0000e-05 - 456ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00018: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0296 - val_loss: 0.2125 - lr: 1.0000e-05 - 532ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0298 - val_loss: 0.2117 - lr: 1.0000e-05 - 473ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0287 - val_loss: 0.2109 - lr: 1.0000e-05 - 617ms/epoch - 11ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0322 - val_loss: 0.2099 - lr: 1.0000e-05 - 515ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0272 - val_loss: 0.2106 - lr: 1.0000e-05 - 456ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0280 - val_loss: 0.2112 - lr: 1.0000e-05 - 488ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0278 - val_loss: 0.2109 - lr: 1.0000e-05 - 491ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0279 - val_loss: 0.2110 - lr: 1.0000e-05 - 469ms/epoch - 8ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0307 - val_loss: 0.2093 - lr: 1.0000e-05 - 502ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0301 - val_loss: 0.2081 - lr: 1.0000e-05 - 521ms/epoch - 9ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0271 - val_loss: 0.2076 - lr: 1.0000e-05 - 517ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0269 - val_loss: 0.2061 - lr: 1.0000e-05 - 458ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0298 - val_loss: 0.2052 - lr: 1.0000e-05 - 472ms/epoch - 8ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0291 - val_loss: 0.2042 - lr: 1.0000e-05 - 499ms/epoch - 9ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0264 - val_loss: 0.2044 - lr: 1.0000e-05 - 479ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0306 - val_loss: 0.2036 - lr: 1.0000e-05 - 515ms/epoch - 9ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0248 - val_loss: 0.2027 - lr: 1.0000e-05 - 472ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0287 - val_loss: 0.2015 - lr: 1.0000e-05 - 478ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0300 - val_loss: 0.2003 - lr: 1.0000e-05 - 472ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0289 - val_loss: 0.2028 - lr: 1.0000e-05 - 479ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0280 - val_loss: 0.2023 - lr: 1.0000e-05 - 492ms/epoch - 8ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0287 - val_loss: 0.2033 - lr: 1.0000e-05 - 495ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0273 - val_loss: 0.2018 - lr: 1.0000e-05 - 468ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0298 - val_loss: 0.1994 - lr: 1.0000e-05 - 479ms/epoch - 8ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0304 - val_loss: 0.1988 - lr: 1.0000e-05 - 596ms/epoch - 10ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0281 - val_loss: 0.2007 - lr: 1.0000e-05 - 495ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0302 - val_loss: 0.1994 - lr: 1.0000e-05 - 488ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0288 - val_loss: 0.1970 - lr: 1.0000e-05 - 488ms/epoch - 8ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0285 - val_loss: 0.1948 - lr: 1.0000e-05 - 499ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0280 - val_loss: 0.1967 - lr: 1.0000e-05 - 499ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0305 - val_loss: 0.1980 - lr: 1.0000e-05 - 459ms/epoch - 8ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0289 - val_loss: 0.1977 - lr: 1.0000e-05 - 518ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0245 - val_loss: 0.1980 - lr: 1.0000e-05 - 479ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0267 - val_loss: 0.1977 - lr: 1.0000e-05 - 499ms/epoch - 9ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.16577
58/58 - 0s - loss: 0.0273 - val_loss: 0.1991 - lr: 1.0000e-05 - 483ms/epoch - 8ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.16577
58/58 - 1s - loss: 0.0266 - val_loss: 0.1989 - lr: 1.0000e-05 - 501ms/epoch - 9ms/step
Epoch 00053: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731

WMA
Prediction vs Close:		55.97% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 70.35376938042184 
RMSE:	 8.387715385039114 
MAPE:	 6.8547592718484545

DEMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 70.24761196199488 
RMSE:	 8.381384847505505 
MAPE:	 6.862692730259403

KAMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 27.01407660930758 
RMSE:	 5.1975067685677505 
MAPE:	 4.263533603346384

MIDPOINT
Prediction vs Close:		50.37% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 37.16076795716489 
RMSE:	 6.095963250969029 
MAPE:	 5.0853544537748006
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16837.838, Time=3.49 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14497.319, Time=3.90 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16084.348, Time=6.59 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-15317.920, Time=11.42 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15304.480, Time=11.34 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15949.053, Time=12.49 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17059.707, Time=11.52 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-15313.920, Time=14.44 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16054.952, Time=13.33 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11445.350, Time=35.07 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 123.603 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8561.853
Date:                Sun, 12 Dec 2021   AIC                         -17059.707
Time:                        16:51:30   BIC                         -16909.600
Sample:                             0   HQIC                        -17002.059
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.003e-07   7.69e-05     -0.001      0.999      -0.000       0.000
x2         -1.001e-07   7.44e-05     -0.001      0.999      -0.000       0.000
x3         -1.006e-07   7.84e-05     -0.001      0.999      -0.000       0.000
x4             1.0000   7.11e-05   1.41e+04      0.000       1.000       1.000
x5         -9.611e-08   6.77e-05     -0.001      0.999      -0.000       0.000
x6         -1.249e-07   4.06e-05     -0.003      0.998   -7.96e-05    7.94e-05
x7             -1e-07   7.89e-05     -0.001      0.999      -0.000       0.000
x8            -0.0002   9.43e-05     -1.838      0.066      -0.000    1.15e-05
x9          2.853e-08   9.89e-05      0.000      1.000      -0.000       0.000
x10        -4.022e-05      0.000     -0.200      0.842      -0.000       0.000
x11            0.0003      7e-05      4.122      0.000       0.000       0.000
x12          7.55e-05      0.000      0.633      0.527      -0.000       0.000
x13        -1.005e-07   7.29e-05     -0.001      0.999      -0.000       0.000
x14        -2.756e-07      0.000     -0.001      0.999      -0.000       0.000
x15        -8.419e-08   8.98e-05     -0.001      0.999      -0.000       0.000
x16        -2.171e-07      0.000     -0.001      0.999      -0.000       0.000
x17        -1.105e-07   9.93e-05     -0.001      0.999      -0.000       0.000
x18         1.263e-07   3.22e-05      0.004      0.997   -6.31e-05    6.33e-05
x19        -8.769e-08      0.000     -0.001      0.999      -0.000       0.000
x20        -5.772e-08      0.000     -0.000      1.000      -0.000       0.000
x21         -9.77e-08      0.000     -0.001      1.000      -0.000       0.000
x22        -3.686e-12   7.09e-07   -5.2e-06      1.000   -1.39e-06    1.39e-06
x23        -9.216e-12    2.4e-05  -3.83e-07      1.000   -4.71e-05    4.71e-05
x24        -3.648e-07      0.000     -0.001      0.999      -0.001       0.001
x25        -1.391e-07      0.001     -0.000      1.000      -0.002       0.002
x26        -3.142e-07      0.000     -0.001      0.999      -0.001       0.001
x27        -3.042e-07   5.47e-05     -0.006      0.996      -0.000       0.000
x28        -1.785e-07      0.000     -0.001      0.999      -0.000       0.000
x29        -1.909e-07      0.000     -0.001      1.000      -0.001       0.001
ma.L1         -1.3901   8.24e-06  -1.69e+05      0.000      -1.390      -1.390
ma.L2          0.4035   2.01e-05   2.01e+04      0.000       0.403       0.404
sigma2      7.538e-11   6.94e-11      1.085      0.278   -6.07e-11    2.11e-10
===================================================================================
Ljung-Box (L1) (Q):                  69.36   Jarque-Bera (JB):           6470073.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                           -12.55
Prob(H) (two-sided):                  0.00   Kurtosis:                       441.48
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 3.58e+22. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

WARNING:tensorflow:Layer lstm_63 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_63 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.02752, saving model to LSTM5.h5
43/43 - 2s - loss: 0.4677 - val_loss: 0.0275 - lr: 0.0010 - 2s/epoch - 42ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.02752
43/43 - 0s - loss: 0.1066 - val_loss: 0.0474 - lr: 0.0010 - 402ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.02752
43/43 - 0s - loss: 0.1599 - val_loss: 0.7391 - lr: 0.0010 - 374ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.02752
43/43 - 0s - loss: 0.0688 - val_loss: 0.1002 - lr: 0.0010 - 381ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.02752 to 0.01627, saving model to LSTM5.h5
43/43 - 0s - loss: 0.0612 - val_loss: 0.0163 - lr: 0.0010 - 433ms/epoch - 10ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0435 - val_loss: 0.0873 - lr: 0.0010 - 392ms/epoch - 9ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0430 - val_loss: 0.0233 - lr: 0.0010 - 365ms/epoch - 8ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0386 - val_loss: 0.0546 - lr: 0.0010 - 439ms/epoch - 10ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0380 - val_loss: 0.0199 - lr: 0.0010 - 382ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00010: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0337 - val_loss: 0.0558 - lr: 0.0010 - 403ms/epoch - 9ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0481 - val_loss: 0.0449 - lr: 1.0000e-04 - 398ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0410 - val_loss: 0.0391 - lr: 1.0000e-04 - 353ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0356 - val_loss: 0.0360 - lr: 1.0000e-04 - 426ms/epoch - 10ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0327 - val_loss: 0.0314 - lr: 1.0000e-04 - 405ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00015: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0284 - val_loss: 0.0296 - lr: 1.0000e-04 - 399ms/epoch - 9ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0340 - val_loss: 0.0291 - lr: 1.0000e-05 - 399ms/epoch - 9ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0287 - val_loss: 0.0288 - lr: 1.0000e-05 - 378ms/epoch - 9ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0312 - val_loss: 0.0289 - lr: 1.0000e-05 - 374ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0345 - val_loss: 0.0288 - lr: 1.0000e-05 - 374ms/epoch - 9ms/step
Epoch 20/500

Epoch 00020: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00020: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0266 - val_loss: 0.0283 - lr: 1.0000e-05 - 424ms/epoch - 10ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0332 - val_loss: 0.0279 - lr: 1.0000e-05 - 372ms/epoch - 9ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0318 - val_loss: 0.0279 - lr: 1.0000e-05 - 390ms/epoch - 9ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0301 - val_loss: 0.0275 - lr: 1.0000e-05 - 405ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0284 - val_loss: 0.0274 - lr: 1.0000e-05 - 360ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0273 - val_loss: 0.0275 - lr: 1.0000e-05 - 369ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0291 - val_loss: 0.0273 - lr: 1.0000e-05 - 437ms/epoch - 10ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0305 - val_loss: 0.0272 - lr: 1.0000e-05 - 377ms/epoch - 9ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0286 - val_loss: 0.0268 - lr: 1.0000e-05 - 366ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0312 - val_loss: 0.0267 - lr: 1.0000e-05 - 340ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0291 - val_loss: 0.0263 - lr: 1.0000e-05 - 384ms/epoch - 9ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0261 - val_loss: 0.0266 - lr: 1.0000e-05 - 490ms/epoch - 11ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0305 - val_loss: 0.0268 - lr: 1.0000e-05 - 397ms/epoch - 9ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0286 - val_loss: 0.0269 - lr: 1.0000e-05 - 462ms/epoch - 11ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0297 - val_loss: 0.0272 - lr: 1.0000e-05 - 378ms/epoch - 9ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0270 - val_loss: 0.0268 - lr: 1.0000e-05 - 362ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0268 - val_loss: 0.0263 - lr: 1.0000e-05 - 346ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0267 - val_loss: 0.0263 - lr: 1.0000e-05 - 343ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0335 - val_loss: 0.0261 - lr: 1.0000e-05 - 376ms/epoch - 9ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0313 - val_loss: 0.0257 - lr: 1.0000e-05 - 373ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0286 - val_loss: 0.0256 - lr: 1.0000e-05 - 380ms/epoch - 9ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0278 - val_loss: 0.0251 - lr: 1.0000e-05 - 428ms/epoch - 10ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0269 - val_loss: 0.0247 - lr: 1.0000e-05 - 341ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0281 - val_loss: 0.0239 - lr: 1.0000e-05 - 365ms/epoch - 8ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0295 - val_loss: 0.0238 - lr: 1.0000e-05 - 362ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0292 - val_loss: 0.0235 - lr: 1.0000e-05 - 403ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0283 - val_loss: 0.0236 - lr: 1.0000e-05 - 412ms/epoch - 10ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0300 - val_loss: 0.0239 - lr: 1.0000e-05 - 397ms/epoch - 9ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0285 - val_loss: 0.0245 - lr: 1.0000e-05 - 357ms/epoch - 8ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0307 - val_loss: 0.0239 - lr: 1.0000e-05 - 381ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0274 - val_loss: 0.0236 - lr: 1.0000e-05 - 348ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0275 - val_loss: 0.0241 - lr: 1.0000e-05 - 367ms/epoch - 9ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0291 - val_loss: 0.0238 - lr: 1.0000e-05 - 359ms/epoch - 8ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0272 - val_loss: 0.0239 - lr: 1.0000e-05 - 345ms/epoch - 8ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0288 - val_loss: 0.0241 - lr: 1.0000e-05 - 411ms/epoch - 10ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.01627
43/43 - 0s - loss: 0.0290 - val_loss: 0.0245 - lr: 1.0000e-05 - 334ms/epoch - 8ms/step
Epoch 00055: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731

WMA
Prediction vs Close:		55.97% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 70.35376938042184 
RMSE:	 8.387715385039114 
MAPE:	 6.8547592718484545

DEMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 70.24761196199488 
RMSE:	 8.381384847505505 
MAPE:	 6.862692730259403

KAMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 27.01407660930758 
RMSE:	 5.1975067685677505 
MAPE:	 4.263533603346384

MIDPOINT
Prediction vs Close:		50.37% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 37.16076795716489 
RMSE:	 6.095963250969029 
MAPE:	 5.0853544537748006

T3
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 104.49209322707955 
RMSE:	 10.222137409909903 
MAPE:	 7.958642954509092
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16736.686, Time=3.56 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-15327.143, Time=3.41 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15166.078, Time=7.35 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14962.662, Time=14.62 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16731.606, Time=5.79 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14848.952, Time=10.56 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16921.745, Time=6.30 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14958.662, Time=17.92 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15003.046, Time=12.85 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-16752.122, Time=4.25 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 86.638 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8492.873
Date:                Sun, 12 Dec 2021   AIC                         -16921.745
Time:                        16:57:41   BIC                         -16771.638
Sample:                             0   HQIC                        -16864.098
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          2.277e-08      0.001   3.25e-05      1.000      -0.001       0.001
x2          2.286e-08      0.001    2.5e-05      1.000      -0.002       0.002
x3          2.286e-08      0.001   3.44e-05      1.000      -0.001       0.001
x4             1.0000      0.000   3190.279      0.000       0.999       1.001
x5          2.174e-08      0.001   4.21e-05      1.000      -0.001       0.001
x6          6.124e-09   3.05e-05      0.000      1.000   -5.97e-05    5.97e-05
x7          2.246e-08      0.001   1.67e-05      1.000      -0.003       0.003
x8            -0.0013      0.001     -1.669      0.095      -0.003       0.000
x9         -5.239e-09      0.000  -1.79e-05      1.000      -0.001       0.001
x10            0.0001    9.9e-05      1.396      0.163   -5.59e-05       0.000
x11           -0.0001      0.001     -0.177      0.859      -0.002       0.001
x12            0.0012      0.001      1.426      0.154      -0.000       0.003
x13         2.284e-08      0.000   6.75e-05      1.000      -0.001       0.001
x14         6.258e-08      0.001   5.07e-05      1.000      -0.002       0.002
x15         2.215e-08      0.000      0.000      1.000      -0.000       0.000
x16         3.243e-08      0.000      0.000      1.000      -0.001       0.001
x17          2.22e-08      0.000      0.000      1.000      -0.000       0.000
x18         7.527e-09      0.000   1.67e-05      1.000      -0.001       0.001
x19         2.477e-08      0.000      0.000      1.000      -0.000       0.000
x20        -2.348e-08      0.000  -5.78e-05      1.000      -0.001       0.001
x21         2.718e-08    5.8e-05      0.000      1.000      -0.000       0.000
x22        -2.176e-10      0.000  -5.27e-07      1.000      -0.001       0.001
x23         -2.69e-09   8.49e-05  -3.17e-05      1.000      -0.000       0.000
x24        -4.516e-08   7.24e-06     -0.006      0.995   -1.42e-05    1.41e-05
x25        -4.213e-08   2.81e-05     -0.002      0.999   -5.51e-05     5.5e-05
x26         7.946e-08      0.001      0.000      1.000      -0.001       0.001
x27         4.528e-08      0.001   6.21e-05      1.000      -0.001       0.001
x28          5.92e-08      0.001   4.12e-05      1.000      -0.003       0.003
x29         3.468e-08      0.000   7.06e-05      1.000      -0.001       0.001
ma.L1         -1.3739   4.46e-06  -3.08e+05      0.000      -1.374      -1.374
ma.L2          0.3968    1.4e-05   2.84e+04      0.000       0.397       0.397
sigma2      7.701e-11   7.39e-11      1.043      0.297   -6.78e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  61.47   Jarque-Bera (JB):           5565463.09
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                            10.97
Prob(H) (two-sided):                  0.00   Kurtosis:                       409.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 6.67e+20. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

WARNING:tensorflow:Layer lstm_64 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_64 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.06427, saving model to LSTM5.h5
90/90 - 2s - loss: 0.2083 - val_loss: 0.0643 - lr: 0.0010 - 2s/epoch - 23ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.06427
90/90 - 1s - loss: 0.1306 - val_loss: 0.1781 - lr: 0.0010 - 704ms/epoch - 8ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.06427
90/90 - 1s - loss: 0.0743 - val_loss: 0.5438 - lr: 0.0010 - 714ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.06427
90/90 - 1s - loss: 0.0549 - val_loss: 0.2448 - lr: 0.0010 - 685ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.06427 to 0.01407, saving model to LSTM5.h5
90/90 - 1s - loss: 0.0472 - val_loss: 0.0141 - lr: 0.0010 - 817ms/epoch - 9ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01407
90/90 - 1s - loss: 0.0396 - val_loss: 0.0237 - lr: 0.0010 - 774ms/epoch - 9ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.01407
90/90 - 1s - loss: 0.0371 - val_loss: 0.1705 - lr: 0.0010 - 868ms/epoch - 10ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01407
90/90 - 1s - loss: 0.0397 - val_loss: 0.0380 - lr: 0.0010 - 741ms/epoch - 8ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.01407
90/90 - 1s - loss: 0.0356 - val_loss: 0.2012 - lr: 0.0010 - 734ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.01407 to 0.00690, saving model to LSTM5.h5
90/90 - 1s - loss: 0.0314 - val_loss: 0.0069 - lr: 0.0010 - 761ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00690
90/90 - 1s - loss: 0.0374 - val_loss: 0.0092 - lr: 0.0010 - 813ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00690
90/90 - 1s - loss: 0.0362 - val_loss: 0.0083 - lr: 0.0010 - 756ms/epoch - 8ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.00690 to 0.00570, saving model to LSTM5.h5
90/90 - 1s - loss: 0.0285 - val_loss: 0.0057 - lr: 0.0010 - 757ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0299 - val_loss: 0.0147 - lr: 0.0010 - 759ms/epoch - 8ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0336 - val_loss: 0.2741 - lr: 0.0010 - 740ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0337 - val_loss: 0.0191 - lr: 0.0010 - 859ms/epoch - 10ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0376 - val_loss: 0.4605 - lr: 0.0010 - 703ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00018: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0255 - val_loss: 0.0977 - lr: 0.0010 - 708ms/epoch - 8ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0282 - val_loss: 0.0758 - lr: 1.0000e-04 - 752ms/epoch - 8ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0250 - val_loss: 0.0615 - lr: 1.0000e-04 - 673ms/epoch - 7ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0231 - val_loss: 0.0523 - lr: 1.0000e-04 - 697ms/epoch - 8ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0223 - val_loss: 0.0422 - lr: 1.0000e-04 - 688ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00023: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0220 - val_loss: 0.0379 - lr: 1.0000e-04 - 680ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0215 - val_loss: 0.0375 - lr: 1.0000e-05 - 746ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0191 - val_loss: 0.0366 - lr: 1.0000e-05 - 808ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0218 - val_loss: 0.0354 - lr: 1.0000e-05 - 830ms/epoch - 9ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0209 - val_loss: 0.0355 - lr: 1.0000e-05 - 721ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00028: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0198 - val_loss: 0.0355 - lr: 1.0000e-05 - 710ms/epoch - 8ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0180 - val_loss: 0.0356 - lr: 1.0000e-05 - 697ms/epoch - 8ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0207 - val_loss: 0.0347 - lr: 1.0000e-05 - 698ms/epoch - 8ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0196 - val_loss: 0.0345 - lr: 1.0000e-05 - 690ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0208 - val_loss: 0.0339 - lr: 1.0000e-05 - 724ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0222 - val_loss: 0.0330 - lr: 1.0000e-05 - 832ms/epoch - 9ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0196 - val_loss: 0.0327 - lr: 1.0000e-05 - 727ms/epoch - 8ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0216 - val_loss: 0.0317 - lr: 1.0000e-05 - 720ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0232 - val_loss: 0.0319 - lr: 1.0000e-05 - 691ms/epoch - 8ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0205 - val_loss: 0.0310 - lr: 1.0000e-05 - 755ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0195 - val_loss: 0.0304 - lr: 1.0000e-05 - 679ms/epoch - 8ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0183 - val_loss: 0.0306 - lr: 1.0000e-05 - 686ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0186 - val_loss: 0.0308 - lr: 1.0000e-05 - 727ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0203 - val_loss: 0.0301 - lr: 1.0000e-05 - 684ms/epoch - 8ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0200 - val_loss: 0.0302 - lr: 1.0000e-05 - 700ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0215 - val_loss: 0.0294 - lr: 1.0000e-05 - 712ms/epoch - 8ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0206 - val_loss: 0.0290 - lr: 1.0000e-05 - 710ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0186 - val_loss: 0.0292 - lr: 1.0000e-05 - 680ms/epoch - 8ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0193 - val_loss: 0.0288 - lr: 1.0000e-05 - 781ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0191 - val_loss: 0.0284 - lr: 1.0000e-05 - 743ms/epoch - 8ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0223 - val_loss: 0.0278 - lr: 1.0000e-05 - 826ms/epoch - 9ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0182 - val_loss: 0.0274 - lr: 1.0000e-05 - 732ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0181 - val_loss: 0.0267 - lr: 1.0000e-05 - 728ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0182 - val_loss: 0.0254 - lr: 1.0000e-05 - 745ms/epoch - 8ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0197 - val_loss: 0.0261 - lr: 1.0000e-05 - 753ms/epoch - 8ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0190 - val_loss: 0.0264 - lr: 1.0000e-05 - 736ms/epoch - 8ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0192 - val_loss: 0.0262 - lr: 1.0000e-05 - 723ms/epoch - 8ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0195 - val_loss: 0.0247 - lr: 1.0000e-05 - 751ms/epoch - 8ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0178 - val_loss: 0.0242 - lr: 1.0000e-05 - 734ms/epoch - 8ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0201 - val_loss: 0.0238 - lr: 1.0000e-05 - 712ms/epoch - 8ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0212 - val_loss: 0.0229 - lr: 1.0000e-05 - 780ms/epoch - 9ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0182 - val_loss: 0.0220 - lr: 1.0000e-05 - 729ms/epoch - 8ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0209 - val_loss: 0.0218 - lr: 1.0000e-05 - 726ms/epoch - 8ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0190 - val_loss: 0.0231 - lr: 1.0000e-05 - 781ms/epoch - 9ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0195 - val_loss: 0.0219 - lr: 1.0000e-05 - 731ms/epoch - 8ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00570
90/90 - 1s - loss: 0.0187 - val_loss: 0.0208 - lr: 1.0000e-05 - 713ms/epoch - 8ms/step
Epoch 00063: early stopping
SMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	52.61% Accuracy
MSE:	 34.39169744803393 
RMSE:	 5.864443490053761 
MAPE:	 4.893666026892695

EMA
Prediction vs Close:		53.73% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 73.04930062485933 
RMSE:	 8.546888359213506 
MAPE:	 6.613879572809731

WMA
Prediction vs Close:		55.97% Accuracy
Prediction vs Prediction:	47.39% Accuracy
MSE:	 70.35376938042184 
RMSE:	 8.387715385039114 
MAPE:	 6.8547592718484545

DEMA
Prediction vs Close:		52.61% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 70.24761196199488 
RMSE:	 8.381384847505505 
MAPE:	 6.862692730259403

KAMA
Prediction vs Close:		49.63% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 27.01407660930758 
RMSE:	 5.1975067685677505 
MAPE:	 4.263533603346384

MIDPOINT
Prediction vs Close:		50.37% Accuracy
Prediction vs Prediction:	46.64% Accuracy
MSE:	 37.16076795716489 
RMSE:	 6.095963250969029 
MAPE:	 5.0853544537748006

T3
Prediction vs Close:		56.34% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 104.49209322707955 
RMSE:	 10.222137409909903 
MAPE:	 7.958642954509092

TEMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 72.80283545670305 
RMSE:	 8.532457761788397 
MAPE:	 7.653550657820228
Runtime: mins: 57.24369045236666

Architecture Used

In [116]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving Experiment5.png to Experiment5 (1).png
In [117]:
img = cv2.imread('Experiment5.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[117]:
<matplotlib.image.AxesImage at 0x7fa5ea16d7d0>

Model Plots

In [118]:
for i in range(len(list(simulation5.keys()))):
  SIM = list(simulation5.keys())[i]
  plot_train(simulation5,SIM)
  plot_test(simulation5,SIM)
----- Train RMSE for SMA ----- 7.636357212019493
----- Train_MSE_LSTM for SMA ----- 58.31395146956212
----- Train MAE LSTM for SMA ----- 6.563417649903971
----- Test RMSE for SMA----- 5.864443490053761
----- Test_MSE_LSTM for SMA----- 34.39169744803393
----- Test_MAE_LSTM for SMA----- 4.893666026892695
----- Train RMSE for EMA ----- 9.308960691198413
----- Train_MSE_LSTM for EMA ----- 86.65674915027724
----- Train MAE LSTM for EMA ----- 8.140100172619418
----- Test RMSE for EMA----- 8.546888359213506
----- Test_MSE_LSTM for EMA----- 73.04930062485933
----- Test_MAE_LSTM for EMA----- 6.613879572809731
----- Train RMSE for WMA ----- 9.79972862979708
----- Train_MSE_LSTM for WMA ----- 96.03468121766456
----- Train MAE LSTM for WMA ----- 8.675562439114087
----- Test RMSE for WMA----- 8.387715385039114
----- Test_MSE_LSTM for WMA----- 70.35376938042184
----- Test_MAE_LSTM for WMA----- 6.8547592718484545
----- Train RMSE for DEMA ----- 11.043189962759177
----- Train_MSE_LSTM for DEMA ----- 121.95204455358504
----- Train MAE LSTM for DEMA ----- 9.832117659380973
----- Test RMSE for DEMA----- 8.381384847505505
----- Test_MSE_LSTM for DEMA----- 70.24761196199488
----- Test_MAE_LSTM for DEMA----- 6.862692730259403
----- Train RMSE for KAMA ----- 9.344372249039948
----- Train_MSE_LSTM for KAMA ----- 87.3172927286279
----- Train MAE LSTM for KAMA ----- 8.318259338837343
----- Test RMSE for KAMA----- 5.1975067685677505
----- Test_MSE_LSTM for KAMA----- 27.01407660930758
----- Test_MAE_LSTM for KAMA----- 4.263533603346384
----- Train RMSE for MIDPOINT ----- 8.517306058165431
----- Train_MSE_LSTM for MIDPOINT ----- 72.54450248846156
----- Train MAE LSTM for MIDPOINT ----- 7.583730384747793
----- Test RMSE for MIDPOINT----- 6.095963250969029
----- Test_MSE_LSTM for MIDPOINT----- 37.16076795716489
----- Test_MAE_LSTM for MIDPOINT----- 5.0853544537748006
----- Train RMSE for T3 ----- 10.856747466888622
----- Train_MSE_LSTM for T3 ----- 117.86896555979253
----- Train MAE LSTM for T3 ----- 9.766406240784287
----- Test RMSE for T3----- 10.222137409909903
----- Test_MSE_LSTM for T3----- 104.49209322707955
----- Test_MAE_LSTM for T3----- 7.958642954509092
----- Train RMSE for TEMA ----- 6.927888385272954
----- Train_MSE_LSTM for TEMA ----- 47.9956374787999
----- Train MAE LSTM for TEMA ----- 4.706679976581726
----- Test RMSE for TEMA----- 8.532457761788397
----- Test_MSE_LSTM for TEMA----- 72.80283545670305
----- Test_MAE_LSTM for TEMA----- 7.653550657820228

Arima w Exogenous Variable Multistep MutiVariate LSTM Hybrid Model Experiment 6

In [121]:
def get_arima_exog(dataframe,original_data, train_len, test_len):    
    

    # prepare train and test data for exogenous vr
    X_value = pd.DataFrame(low_vol.iloc[:, :])
    y_value = pd.DataFrame(low_vol.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    X_scale_dataset = X_scaler.fit_transform(X_value)
    y_scale_dataset = y_scaler.fit_transform(y_value)
    # Get data and check shape
    # X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X_scale_dataset)
    y_train, y_test, = split_train_test(y_scale_dataset)
    yc_train,yc_test = split_train_test(low_vol_data)
    yc = yc_test.values.tolist()
    y_train_list = y_train.flatten().tolist()
    y_test_list = y_test.flatten().tolist()
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)

    # Initialize model
    model = auto_arima(y_train_list,exogenous  = X_train,trace=True, error_action='ignore', start_p=1,start_q=1,max_p=3,max_q=3,d=3,
            suppress_warnings=True,stepwise=True,seasonal=True)

      # Determine model parameters
    print(model.summary())
    model.fit(y_train_list,maxiter=200)
    order = model.get_params()['order']
    print('ARIMA order:', order, '\n')

      # Genereate predictions
    prediction = []
    for i in range(len(y_test_list)):
        model = pmdarima.ARIMA(order=order)
        model.fit(y_train_list)
        # print('working on', i+1, 'of', len(y_test), '-- ' + str(int(100 * (i + 1) / len(y_test))) + '% complete')

        prediction.append(model.predict()[0])
        y_train_list.append(y_test_list[i])

    predictionte = y_scaler.inverse_transform(np.array(prediction).reshape(-1,1))
    y_test_ = y_scaler.inverse_transform(np.array(y_test_list).reshape(-1,1))

    # Generate error data
    mse = mean_squared_error(yc_test, predictionte)
    rmse = mse ** 0.5
    mae = mean_absolute_error(y_test_ , predictionte )
    return yc,predictionte.flatten().tolist(), mse, rmse, mae
In [122]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det =20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # Option 1
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    # model.add(Dense(units=64,activation='relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    # ## Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()


    # # option 2
    model = Sequential()
    model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    model.add(Dense(64))
    model.add(Dense(units=output_dim))
    model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM6.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()

    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    # class Double_Tanh(Activation):
    #     def __init__(self, activation, **kwargs):
    #         super(Double_Tanh, self).__init__(activation, **kwargs)
    #         self.__name__ = 'double_tanh'

    # def double_tanh(x):
    #     return (K.tanh(x) * 2)

    # get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
    #     # Model Generation
    # model = Sequential()
    # #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    # model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    # model.add(Dense(1))
    # model.add(Activation(double_tanh))
    # model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(x_train.shape[1], 1)))
    # model.add(LSTM(units=int(lstm_len/2)))
    # model.add(Dense(1, activation='sigmoid'))
    # model.compile(loss='mean_squared_error', optimizer='adam')
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [123]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation6 = {}
    imgfile = 'Experiment6'
    for ma in optimized_period:
                print(ma)
                print(functions[ma])
                print ( int( optimized_period[ma]))
              # if ma == 'SMA':
                low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
                low_vol = low_vol.fillna(0)
                low_vol_data = df['close']
                high_vol = pd.DataFrame()
                df2 = df.copy()
                for i in df2.columns:
                  if i in low_vol.columns:
                    high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
                high_vol_data = df['close']
                ## *****************************************************
                # Generate ARIMA and LSTM predictions
                print('\nWorking on ' + ma + ' predictions')
                try:
                  print('parameters used : ', train_len, test_len)
                  low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima_exog(low_vol,low_vol_data, train_len, test_len)
                except:
                    print('ARIMA error, skipping to next MA type')
                    continue
                Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
                final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
                mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
                rmse_ftr = mse_ftr ** 0.5
                mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
                mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

                final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
                mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
                rmse = mse ** 0.5
                mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                # Generate prediction accuracy
                actual = df['close'].tail(test_len).values
                result_1 = []
                result_2 = []
                for i in range(1, len(final_prediction)):
                    # Compare prediction to previous close price
                    if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                        result_1.append(1)
                    elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                        result_1.append(1)
                    else:
                        result_1.append(0)

                    # Compare prediction to previous prediction
                    if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                        result_2.append(1)
                    elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                        result_2.append(1)
                    else:
                        result_2.append(0)

                accuracy_1 = np.mean(result_1)
                accuracy_2 = np.mean(result_2)

                simulation6[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                              'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                  'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                              'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                  'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                              'rmse': rmse_ftr, 'mae' : mae_ftr},
                                  'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                            'rmse': rmse, 'mae': mae },
                                  'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

                # save simulation data here as checkpoint
                with open('simulation6_data.json', 'w') as fp:
                    json.dump(simulation6, fp)

                for ma in simulation6.keys():
                    print('\n' + ma)
                    print('Prediction vs Close:\t\t' + str(round(100*simulation6[ma]['accuracy']['prediction vs close'], 2))
                          + '% Accuracy')
                    print('Prediction vs Prediction:\t' + str(round(100*simulation6[ma]['accuracy']['prediction vs prediction'], 2))
                          + '% Accuracy')
                    print('MSE:\t', simulation6[ma]['final']['mse'],
                          '\nRMSE:\t', simulation6[ma]['final']['rmse'],
                          '\nMAPE:\t', simulation6[ma]['final']['mae'])#,
                          # '\nMAPE:\t', simulation[ma]['final']['mape'])
              # else:
              #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.787, Time=3.67 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.588, Time=5.52 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14596.280, Time=5.65 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.588, Time=8.39 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16924.805, Time=10.47 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14482.349, Time=11.23 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17215.608, Time=20.61 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.588, Time=10.76 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15570.350, Time=19.25 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11671.292, Time=28.75 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 124.319 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8639.804
Date:                Sun, 12 Dec 2021   AIC                         -17215.608
Time:                        17:11:20   BIC                         -17065.501
Sample:                             0   HQIC                        -17157.961
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -4.057e-09   5.82e-05  -6.97e-05      1.000      -0.000       0.000
x2         -4.057e-09   5.81e-05  -6.99e-05      1.000      -0.000       0.000
x3         -4.111e-09   5.49e-05  -7.49e-05      1.000      -0.000       0.000
x4             1.0000   5.71e-05   1.75e+04      0.000       1.000       1.000
x5         -3.706e-09   5.43e-05  -6.82e-05      1.000      -0.000       0.000
x6         -1.082e-08      0.000  -6.08e-05      1.000      -0.000       0.000
x7         -4.025e-09   5.63e-05  -7.15e-05      1.000      -0.000       0.000
x8         -4.035e-09   5.19e-05  -7.78e-05      1.000      -0.000       0.000
x9         -1.522e-10    2.9e-05  -5.25e-06      1.000   -5.68e-05    5.68e-05
x10        -6.396e-10   1.04e-05  -6.15e-05      1.000   -2.04e-05    2.04e-05
x11        -3.921e-09   5.06e-05  -7.75e-05      1.000   -9.91e-05    9.91e-05
x12        -4.102e-09   5.29e-05  -7.76e-05      1.000      -0.000       0.000
x13        -4.087e-09   5.75e-05  -7.11e-05      1.000      -0.000       0.000
x14        -3.619e-08      0.000     -0.000      1.000      -0.000       0.000
x15        -4.806e-09   4.61e-05     -0.000      1.000   -9.03e-05    9.03e-05
x16        -3.507e-09      0.000  -2.98e-05      1.000      -0.000       0.000
x17        -3.121e-09   6.02e-05  -5.18e-05      1.000      -0.000       0.000
x18        -1.172e-08      0.000     -0.000      1.000      -0.000       0.000
x19        -5.433e-09   6.06e-05  -8.96e-05      1.000      -0.000       0.000
x20        -1.393e-08   4.79e-05     -0.000      1.000   -9.39e-05    9.39e-05
x21        -4.216e-09   6.63e-05  -6.36e-05      1.000      -0.000       0.000
x22        -3.479e-11   1.66e-08     -0.002      0.998   -3.25e-08    3.24e-08
x23        -9.221e-10    1.4e-07     -0.007      0.995   -2.74e-07    2.73e-07
x24        -8.085e-08      0.001  -6.96e-05      1.000      -0.002       0.002
x25        -9.642e-08      0.001     -0.000      1.000      -0.002       0.002
x26        -5.019e-08      0.000     -0.000      1.000      -0.000       0.000
x27        -2.457e-08   7.65e-05     -0.000      1.000      -0.000       0.000
x28        -3.411e-08      0.000     -0.000      1.000      -0.000       0.000
x29        -1.507e-08   4.36e-05     -0.000      1.000   -8.54e-05    8.54e-05
ma.L1         -1.3898   8.03e-07  -1.73e+06      0.000      -1.390      -1.390
ma.L2          0.4031   8.36e-07   4.82e+05      0.000       0.403       0.403
sigma2      7.528e-11   7.24e-11      1.040      0.298   -6.66e-11    2.17e-10
===================================================================================
Ljung-Box (L1) (Q):                  89.12   Jarque-Bera (JB):           1533103.33
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             5.56
Prob(H) (two-sided):                  0.00   Kurtosis:                       216.50
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.08e+25. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04685, saving model to LSTM6.h5
48/48 - 3s - loss: 0.1506 - accuracy: 0.0000e+00 - val_loss: 0.0468 - val_accuracy: 0.0037 - lr: 0.0010 - 3s/epoch - 71ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.04685 to 0.00855, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0267 - accuracy: 0.0000e+00 - val_loss: 0.0085 - val_accuracy: 0.0037 - lr: 0.0010 - 291ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0284 - accuracy: 0.0000e+00 - val_loss: 0.0544 - val_accuracy: 0.0037 - lr: 0.0010 - 249ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0260 - accuracy: 0.0000e+00 - val_loss: 0.0121 - val_accuracy: 0.0037 - lr: 0.0010 - 273ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0112 - accuracy: 0.0000e+00 - val_loss: 0.0529 - val_accuracy: 0.0037 - lr: 0.0010 - 283ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0128 - accuracy: 0.0000e+00 - val_loss: 0.0290 - val_accuracy: 0.0037 - lr: 0.0010 - 252ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0119 - accuracy: 0.0000e+00 - val_loss: 0.0662 - val_accuracy: 0.0037 - lr: 0.0010 - 279ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0206 - accuracy: 0.0000e+00 - val_loss: 0.0188 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 303ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.00855
48/48 - 0s - loss: 0.0037 - accuracy: 0.0000e+00 - val_loss: 0.0127 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 267ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.00855 to 0.00839, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 275ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.00839 to 0.00607, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0026 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 277ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.00607 to 0.00490, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 290ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.00490 to 0.00436, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 319ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.00436 to 0.00414, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 269ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss improved from 0.00414 to 0.00405, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 319ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.00405 to 0.00401, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 288ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.00401 to 0.00399, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 284ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.00399 to 0.00395, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 298ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.00395 to 0.00392, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 289ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss improved from 0.00392 to 0.00388, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 295ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss improved from 0.00388 to 0.00383, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 283ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss improved from 0.00383 to 0.00379, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 301ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss improved from 0.00379 to 0.00375, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 298ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss improved from 0.00375 to 0.00371, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 322ms/epoch - 7ms/step
Epoch 25/500

Epoch 00025: val_loss improved from 0.00371 to 0.00368, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 271ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss improved from 0.00368 to 0.00365, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 314ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss improved from 0.00365 to 0.00362, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 293ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.00362 to 0.00360, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 290ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss improved from 0.00360 to 0.00359, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 326ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss improved from 0.00359 to 0.00358, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 269ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00031: val_loss improved from 0.00358 to 0.00358, saving model to LSTM6.h5
48/48 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 304ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.2863e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.2266e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.2009e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.1823e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00036: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.1660e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.1507e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.1360e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.1219e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.1081e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 289ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0946e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 316ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0813e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 299ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0682e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 295ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0553e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 283ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0426e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 287ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0299e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0173e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 304ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00358
48/48 - 0s - loss: 9.0048e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 294ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9923e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9799e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9674e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9550e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9426e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 6ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9302e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9177e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.9053e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 289ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8928e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8802e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 250ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8677e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8551e-04 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8424e-04 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8297e-04 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8169e-04 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 6ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.8040e-04 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 303ms/epoch - 6ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7911e-04 - accuracy: 0.0000e+00 - val_loss: 0.0040 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7781e-04 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7650e-04 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 302ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7518e-04 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7385e-04 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7252e-04 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.7117e-04 - accuracy: 0.0000e+00 - val_loss: 0.0041 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 289ms/epoch - 6ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6981e-04 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6844e-04 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6707e-04 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 289ms/epoch - 6ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6568e-04 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 292ms/epoch - 6ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6427e-04 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6286e-04 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 294ms/epoch - 6ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6144e-04 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.6000e-04 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 311ms/epoch - 6ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.5855e-04 - accuracy: 0.0000e+00 - val_loss: 0.0043 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.00358
48/48 - 0s - loss: 8.5709e-04 - accuracy: 0.0000e+00 - val_loss: 0.0044 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 305ms/epoch - 6ms/step
Epoch 00081: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.45 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.48 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15952.568, Time=15.38 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=8.39 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16628.634, Time=10.82 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16462.206, Time=25.75 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16848.298, Time=13.21 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.023, Time=6.97 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.619, Time=3.98 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=8.34 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=18.69 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.994, Time=4.30 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.667, Time=4.98 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 129.764 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        17:17:37   BIC                         -16911.966
Sample:                             0   HQIC                        -17010.204
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -2.316e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x2         -2.309e-10   6.24e-05   -3.7e-06      1.000      -0.000       0.000
x3         -2.325e-10   6.26e-05  -3.71e-06      1.000      -0.000       0.000
x4             1.0000   6.25e-05    1.6e+04      0.000       1.000       1.000
x5         -2.107e-10   5.96e-05  -3.54e-06      1.000      -0.000       0.000
x6         -7.997e-10      0.000  -7.41e-06      1.000      -0.000       0.000
x7         -2.295e-10   6.22e-05  -3.69e-06      1.000      -0.000       0.000
x8         -2.246e-10   6.15e-05  -3.65e-06      1.000      -0.000       0.000
x9         -1.167e-11   1.25e-05  -9.33e-07      1.000   -2.45e-05    2.45e-05
x10        -4.454e-11   2.66e-05  -1.68e-06      1.000   -5.21e-05    5.21e-05
x11        -2.221e-10   6.11e-05  -3.63e-06      1.000      -0.000       0.000
x12        -2.266e-10   6.18e-05  -3.66e-06      1.000      -0.000       0.000
x13        -2.315e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x14        -1.767e-09      0.000  -1.02e-05      1.000      -0.000       0.000
x15         -2.11e-10   5.93e-05  -3.56e-06      1.000      -0.000       0.000
x16        -5.283e-10   9.45e-05  -5.59e-06      1.000      -0.000       0.000
x17        -2.098e-10   6.01e-05  -3.49e-06      1.000      -0.000       0.000
x18         -3.82e-11   2.41e-05  -1.58e-06      1.000   -4.73e-05    4.73e-05
x19        -2.645e-10   6.61e-05     -4e-06      1.000      -0.000       0.000
x20        -2.417e-10   6.21e-05  -3.89e-06      1.000      -0.000       0.000
x21        -4.824e-10   8.83e-05  -5.46e-06      1.000      -0.000       0.000
x22        -3.758e-13   1.19e-11     -0.032      0.975   -2.36e-11    2.29e-11
x23        -1.089e-11   8.42e-11     -0.129      0.897   -1.76e-10    1.54e-10
x24        -2.538e-09      0.000  -1.44e-05      1.000      -0.000       0.000
x25        -2.038e-09      0.000  -1.49e-05      1.000      -0.000       0.000
x26         -3.16e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x27        -2.955e-09      0.000  -1.32e-05      1.000      -0.000       0.000
x28        -1.664e-09      0.000  -9.94e-06      1.000      -0.000       0.000
x29        -1.568e-09      0.000  -9.63e-06      1.000      -0.000       0.000
ar.L1         -0.4923    6.2e-10  -7.94e+08      0.000      -0.492      -0.492
ar.L2         -0.1923    3.6e-10  -5.35e+08      0.000      -0.192      -0.192
ar.L3         -0.0462   1.71e-10  -2.71e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.41e-09  -5.04e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  51.79   Jarque-Bera (JB):           4012066.18
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.44
Prob(H) (two-sided):                  0.00   Kurtosis:                       348.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 5.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.00799, saving model to LSTM6.h5
16/16 - 3s - loss: 0.0776 - accuracy: 0.0000e+00 - val_loss: 0.0080 - val_accuracy: 0.0037 - lr: 0.0010 - 3s/epoch - 218ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0318 - accuracy: 0.0000e+00 - val_loss: 0.1262 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 100ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.00799
16/16 - 0s - loss: 0.0517 - accuracy: 0.0000e+00 - val_loss: 0.0132 - val_accuracy: 0.0037 - lr: 0.0010 - 113ms/epoch - 7ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.00799 to 0.00501, saving model to LSTM6.h5
16/16 - 0s - loss: 0.0237 - accuracy: 0.0000e+00 - val_loss: 0.0050 - val_accuracy: 0.0037 - lr: 0.0010 - 135ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0243 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 0.0010 - 105ms/epoch - 7ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0106 - accuracy: 0.0000e+00 - val_loss: 0.0249 - val_accuracy: 0.0037 - lr: 0.0010 - 104ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0154 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 0.0010 - 107ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0048 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 0.0010 - 119ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00009: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0070 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 0.0010 - 110ms/epoch - 7ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0079 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 99ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 100ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0067 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 120ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 114ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00014: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 121ms/epoch - 8ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00019: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 127ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 120ms/epoch - 8ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 121ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 122ms/epoch - 8ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00501
16/16 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 00054: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.57 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.37 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14597.576, Time=5.63 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=8.21 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15338.693, Time=11.46 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15153.472, Time=27.75 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17112.658, Time=16.25 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.587, Time=10.68 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15106.216, Time=14.92 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-12251.715, Time=36.41 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 140.271 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8588.329
Date:                Sun, 12 Dec 2021   AIC                         -17112.658
Time:                        17:28:56   BIC                         -16962.551
Sample:                             0   HQIC                        -17055.011
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          -4.53e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x2         -4.512e-09   3.25e-06     -0.001      0.999   -6.38e-06    6.37e-06
x3         -4.538e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x4             1.0000   3.26e-06   3.07e+05      0.000       1.000       1.000
x5         -4.105e-09   3.11e-06     -0.001      0.999    -6.1e-06    6.09e-06
x6         -1.488e-08   5.45e-06     -0.003      0.998   -1.07e-05    1.07e-05
x7         -4.481e-09   3.24e-06     -0.001      0.999   -6.36e-06    6.36e-06
x8         -4.365e-09    3.2e-06     -0.001      0.999   -6.29e-06    6.28e-06
x9         -4.628e-10   8.38e-07     -0.001      1.000   -1.64e-06    1.64e-06
x10        -7.326e-10    1.3e-06     -0.001      1.000   -2.55e-06    2.54e-06
x11        -4.347e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x12        -4.345e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x13         -4.52e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x14        -3.586e-08      9e-06     -0.004      0.997   -1.77e-05    1.76e-05
x15        -3.757e-09   2.98e-06     -0.001      0.999   -5.84e-06    5.83e-06
x16         -1.24e-08   5.36e-06     -0.002      0.998   -1.05e-05    1.05e-05
x17        -4.515e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x18        -2.632e-10   7.07e-07     -0.000      1.000   -1.39e-06    1.39e-06
x19        -4.642e-09    3.3e-06     -0.001      0.999   -6.47e-06    6.46e-06
x20        -3.919e-10   6.91e-07     -0.001      1.000   -1.36e-06    1.35e-06
x21         -7.69e-09   4.13e-06     -0.002      0.999   -8.11e-06    8.09e-06
x22        -6.998e-12   2.69e-13    -25.970      0.000   -7.53e-12   -6.47e-12
x23         -1.81e-10   2.22e-12    -81.582      0.000   -1.85e-10   -1.77e-10
x24        -4.955e-08    8.9e-06     -0.006      0.996   -1.75e-05    1.74e-05
x25        -4.901e-08    8.4e-06     -0.006      0.995   -1.65e-05    1.64e-05
x26        -6.446e-08    1.2e-05     -0.005      0.996   -2.37e-05    2.35e-05
x27         -5.73e-08   1.14e-05     -0.005      0.996   -2.24e-05    2.23e-05
x28        -2.997e-08   8.22e-06     -0.004      0.997   -1.61e-05    1.61e-05
x29        -3.486e-08   8.89e-06     -0.004      0.997   -1.75e-05    1.74e-05
ma.L1         -1.3902   3.62e-10  -3.84e+09      0.000      -1.390      -1.390
ma.L2          0.4033   3.72e-10   1.08e+09      0.000       0.403       0.403
sigma2      8.541e-11   6.95e-11      1.229      0.219   -5.08e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  66.92   Jarque-Bera (JB):           6039240.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                            12.14
Prob(H) (two-sided):                  0.00   Kurtosis:                       426.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.94e+30. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.12839, saving model to LSTM6.h5
17/17 - 4s - loss: 0.1323 - accuracy: 0.0000e+00 - val_loss: 0.1284 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 231ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.12839 to 0.10391, saving model to LSTM6.h5
17/17 - 0s - loss: 0.0454 - accuracy: 0.0000e+00 - val_loss: 0.1039 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 146ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.10391 to 0.00557, saving model to LSTM6.h5
17/17 - 0s - loss: 0.0245 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 0.0010 - 144ms/epoch - 8ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0150 - accuracy: 0.0000e+00 - val_loss: 0.0089 - val_accuracy: 0.0037 - lr: 0.0010 - 106ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0242 - accuracy: 0.0000e+00 - val_loss: 0.0156 - val_accuracy: 0.0037 - lr: 0.0010 - 110ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0181 - accuracy: 0.0000e+00 - val_loss: 0.0157 - val_accuracy: 0.0037 - lr: 0.0010 - 107ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0165 - accuracy: 0.0000e+00 - val_loss: 0.0437 - val_accuracy: 0.0037 - lr: 0.0010 - 121ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00008: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0416 - accuracy: 0.0000e+00 - val_loss: 0.0587 - val_accuracy: 0.0037 - lr: 0.0010 - 124ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0598 - accuracy: 0.0000e+00 - val_loss: 0.0388 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 134ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0068 - accuracy: 0.0000e+00 - val_loss: 0.0267 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 117ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0052 - accuracy: 0.0000e+00 - val_loss: 0.0229 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 130ms/epoch - 8ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0039 - accuracy: 0.0000e+00 - val_loss: 0.0204 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 123ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00013: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0176 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 112ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0173 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0171 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 118ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0169 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 121ms/epoch - 7ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0166 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00018: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0164 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0162 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0159 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0157 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0155 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0153 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0150 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0148 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 128ms/epoch - 8ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0146 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 138ms/epoch - 8ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0021 - accuracy: 0.0000e+00 - val_loss: 0.0144 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 121ms/epoch - 7ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0142 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0140 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0138 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 128ms/epoch - 8ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0135 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 129ms/epoch - 8ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0133 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 128ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0131 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0130 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0128 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0126 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0124 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 106ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0122 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0120 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0119 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0117 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 133ms/epoch - 8ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0115 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0113 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 110ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0112 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0110 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0109 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0107 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0106 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 131ms/epoch - 8ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0104 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 131ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0103 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 139ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0102 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 109ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 125ms/epoch - 7ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00557
17/17 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0099 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 00053: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704

WMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.57086226636761 
RMSE:	 8.400646538592587 
MAPE:	 6.6664001460728475
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.776, Time=3.85 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.586, Time=5.60 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16271.755, Time=7.45 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.586, Time=8.32 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15152.908, Time=11.62 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14481.105, Time=13.78 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16088.109, Time=23.23 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.021, Time=6.97 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.615, Time=3.80 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=8.00 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=20.06 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.981, Time=4.57 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.666, Time=5.26 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 122.542 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        17:35:26   BIC                         -16911.965
Sample:                             0   HQIC                        -17010.203
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -2.8e-10   6.02e-05  -4.65e-06      1.000      -0.000       0.000
x2         -2.817e-10   6.04e-05  -4.66e-06      1.000      -0.000       0.000
x3         -2.805e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x4             1.0000   6.03e-05   1.66e+04      0.000       1.000       1.000
x5           -2.6e-10    5.8e-05  -4.48e-06      1.000      -0.000       0.000
x6         -1.389e-09      0.000  -1.08e-05      1.000      -0.000       0.000
x7         -2.789e-10   6.01e-05  -4.64e-06      1.000      -0.000       0.000
x8         -2.763e-10   5.99e-05  -4.62e-06      1.000      -0.000       0.000
x9         -2.224e-12    1.6e-06  -1.39e-06      1.000   -3.13e-06    3.13e-06
x10        -1.345e-10   4.12e-05  -3.26e-06      1.000   -8.08e-05    8.08e-05
x11          -2.9e-10   6.12e-05  -4.74e-06      1.000      -0.000       0.000
x12        -2.602e-10   5.82e-05  -4.47e-06      1.000      -0.000       0.000
x13        -2.807e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x14         -1.87e-09      0.000   -1.2e-05      1.000      -0.000       0.000
x15        -2.844e-10   6.05e-05   -4.7e-06      1.000      -0.000       0.000
x16        -7.962e-11    3.2e-05  -2.48e-06      1.000   -6.28e-05    6.28e-05
x17        -2.445e-10   5.61e-05  -4.36e-06      1.000      -0.000       0.000
x18          -6.4e-10   9.15e-05  -6.99e-06      1.000      -0.000       0.000
x19        -2.923e-10   6.14e-05  -4.76e-06      1.000      -0.000       0.000
x20        -4.336e-10   7.41e-05  -5.86e-06      1.000      -0.000       0.000
x21         -4.55e-10    7.5e-05  -6.07e-06      1.000      -0.000       0.000
x22        -3.587e-13   1.42e-11     -0.025      0.980   -2.82e-11    2.75e-11
x23        -1.088e-11   9.56e-11     -0.114      0.909   -1.98e-10    1.76e-10
x24        -2.146e-09      0.000  -1.63e-05      1.000      -0.000       0.000
x25        -1.637e-09      0.000  -1.35e-05      1.000      -0.000       0.000
x26        -3.147e-09      0.000  -1.56e-05      1.000      -0.000       0.000
x27         -2.58e-09      0.000  -1.41e-05      1.000      -0.000       0.000
x28        -2.444e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x29        -1.666e-09      0.000  -1.13e-05      1.000      -0.000       0.000
ar.L1         -0.4923    5.1e-10  -9.65e+08      0.000      -0.492      -0.492
ar.L2         -0.1923   2.96e-10  -6.49e+08      0.000      -0.192      -0.192
ar.L3         -0.0462    1.4e-10  -3.29e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.16e-09  -6.12e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  54.06   Jarque-Bera (JB):           4126495.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.48
Prob(H) (two-sided):                  0.00   Kurtosis:                       353.58
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 2.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.02054, saving model to LSTM6.h5
10/10 - 4s - loss: 0.2598 - accuracy: 0.0000e+00 - val_loss: 0.0205 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 394ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.02054
10/10 - 0s - loss: 0.1598 - accuracy: 0.0000e+00 - val_loss: 0.0390 - val_accuracy: 0.0037 - lr: 0.0010 - 85ms/epoch - 9ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.02054 to 0.01761, saving model to LSTM6.h5
10/10 - 0s - loss: 0.1513 - accuracy: 0.0000e+00 - val_loss: 0.0176 - val_accuracy: 0.0037 - lr: 0.0010 - 125ms/epoch - 13ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.01761 to 0.00421, saving model to LSTM6.h5
10/10 - 0s - loss: 0.0546 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 0.0010 - 123ms/epoch - 12ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0137 - accuracy: 0.0000e+00 - val_loss: 0.0105 - val_accuracy: 0.0037 - lr: 0.0010 - 80ms/epoch - 8ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0130 - val_accuracy: 0.0037 - lr: 0.0010 - 84ms/epoch - 8ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0039 - accuracy: 0.0000e+00 - val_loss: 0.0112 - val_accuracy: 0.0037 - lr: 0.0010 - 77ms/epoch - 8ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 0.0010 - 83ms/epoch - 8ms/step
Epoch 9/500

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00009: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 0.0010 - 91ms/epoch - 9ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0102 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 85ms/epoch - 8ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 88ms/epoch - 9ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 96ms/epoch - 10ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 93ms/epoch - 9ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00014: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 89ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00019: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 88ms/epoch - 9ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 83ms/epoch - 8ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 103ms/epoch - 10ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 98ms/epoch - 10ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 92ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 88ms/epoch - 9ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 100ms/epoch - 10ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 88ms/epoch - 9ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 103ms/epoch - 10ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 89ms/epoch - 9ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 92ms/epoch - 9ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 66ms/epoch - 7ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 79ms/epoch - 8ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00421
10/10 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 86ms/epoch - 9ms/step
Epoch 00054: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704

WMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.57086226636761 
RMSE:	 8.400646538592587 
MAPE:	 6.6664001460728475

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 329.6035699397079 
RMSE:	 18.15498746735199 
MAPE:	 16.799244301034683
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.104, Time=4.07 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.591, Time=5.58 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16779.655, Time=11.30 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.590, Time=8.28 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16989.430, Time=4.15 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-16990.286, Time=4.14 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.543, Time=3.85 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-16987.154, Time=4.23 sec
 ARIMA(2,3,0)(0,0,0)[0] intercept   : AIC=-16533.935, Time=16.23 sec

Best model:  ARIMA(2,3,0)(0,0,0)[0]          
Total fit time: 61.869 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(2, 3, 0)   Log Likelihood                8527.143
Date:                Sun, 12 Dec 2021   AIC                         -16990.286
Time:                        17:45:39   BIC                         -16840.179
Sample:                             0   HQIC                        -16932.639
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -1.1e-16        nan        nan        nan         nan         nan
x2         -3.811e-16         -0        inf      0.000   -3.81e-16   -3.81e-16
x3          8.776e-16   4.38e-27      2e+11      0.000    8.78e-16    8.78e-16
x4             1.0000   4.36e-27   2.29e+26      0.000       1.000       1.000
x5          6.686e-16   4.14e-27   1.61e+11      0.000    6.69e-16    6.69e-16
x6         -5.238e-17   9.44e-27  -5.55e+09      0.000   -5.24e-17   -5.24e-17
x7         -1.709e-16   4.37e-27  -3.91e+10      0.000   -1.71e-16   -1.71e-16
x8          1.439e-15   4.33e-27   3.32e+11      0.000    1.44e-15    1.44e-15
x9         -2.924e-16   5.73e-28   -5.1e+11      0.000   -2.92e-16   -2.92e-16
x10        -1.028e-16   1.78e-27  -5.76e+10      0.000   -1.03e-16   -1.03e-16
x11        -4.338e-16   4.31e-27  -1.01e+11      0.000   -4.34e-16   -4.34e-16
x12          1.72e-16   4.33e-27   3.97e+10      0.000    1.72e-16    1.72e-16
x13        -3.011e-16   4.36e-27  -6.91e+10      0.000   -3.01e-16   -3.01e-16
x14        -2.611e-16   1.27e-26  -2.06e+10      0.000   -2.61e-16   -2.61e-16
x15          1.53e-14   4.46e-27   3.43e+12      0.000    1.53e-14    1.53e-14
x16        -1.401e-14   5.45e-27  -2.57e+12      0.000    -1.4e-14    -1.4e-14
x17         2.316e-14   4.12e-27   5.62e+12      0.000    2.32e-14    2.32e-14
x18        -3.727e-15   3.71e-27  -1.01e+12      0.000   -3.73e-15   -3.73e-15
x19        -1.361e-14   4.94e-27  -2.75e+12      0.000   -1.36e-14   -1.36e-14
x20        -5.277e-15   6.08e-27  -8.68e+11      0.000   -5.28e-15   -5.28e-15
x21         1.178e-18   3.12e-27   3.77e+08      0.000    1.18e-18    1.18e-18
x22        -8.779e-17   1.74e-29  -5.05e+12      0.000   -8.78e-17   -8.78e-17
x23         3.183e-17   5.91e-29   5.39e+11      0.000    3.18e-17    3.18e-17
x24        -1.683e-16   1.41e-26  -1.19e+10      0.000   -1.68e-16   -1.68e-16
x25         8.988e-17   1.48e-30   6.08e+13      0.000    8.99e-17    8.99e-17
x26         4.435e-17   1.58e-26    2.8e+09      0.000    4.44e-17    4.44e-17
x27         1.538e-16   8.87e-27   1.73e+10      0.000    1.54e-16    1.54e-16
x28         1.635e-16   1.22e-26   1.34e+10      0.000    1.63e-16    1.63e-16
x29         1.474e-16   6.34e-27   2.33e+10      0.000    1.47e-16    1.47e-16
ar.L1         -0.9879   1.21e-22  -8.16e+21      0.000      -0.988      -0.988
ar.L2         -0.4879   1.29e-22  -3.79e+21      0.000      -0.488      -0.488
sigma2          1e-10   6.99e-11      1.432      0.152   -3.69e-11    2.37e-10
===================================================================================
Ljung-Box (L1) (Q):                  57.29   Jarque-Bera (JB):            559955.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.13   Skew:                             0.64
Prob(H) (two-sided):                  0.00   Kurtosis:                       132.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/mlemodel.py:2968: RuntimeWarning: divide by zero encountered in true_divide
  return self.params / self.bse
ARIMA order: (2, 3, 0) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.03815, saving model to LSTM6.h5
45/45 - 4s - loss: 0.1761 - accuracy: 0.0000e+00 - val_loss: 0.0382 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 94ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.03815 to 0.00774, saving model to LSTM6.h5
45/45 - 0s - loss: 0.0287 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 0.0010 - 275ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.00774
45/45 - 0s - loss: 0.0239 - accuracy: 0.0000e+00 - val_loss: 0.0861 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 264ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.00774
45/45 - 0s - loss: 0.0472 - accuracy: 0.0000e+00 - val_loss: 0.0105 - val_accuracy: 0.0037 - lr: 0.0010 - 292ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00774
45/45 - 0s - loss: 0.0134 - accuracy: 0.0000e+00 - val_loss: 0.0643 - val_accuracy: 0.0037 - lr: 0.0010 - 275ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00774
45/45 - 0s - loss: 0.0169 - accuracy: 0.0000e+00 - val_loss: 0.0183 - val_accuracy: 0.0037 - lr: 0.0010 - 289ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.00774
45/45 - 0s - loss: 0.0102 - accuracy: 0.0000e+00 - val_loss: 0.0353 - val_accuracy: 0.0037 - lr: 0.0010 - 265ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00774
45/45 - 0s - loss: 0.0220 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 279ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.00774 to 0.00573, saving model to LSTM6.h5
45/45 - 0s - loss: 0.0028 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 285ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.00573 to 0.00486, saving model to LSTM6.h5
45/45 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 282ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0050 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 286ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 256ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0064 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 279ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 281ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00015: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 259ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00020: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 289ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 305ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 303ms/epoch - 7ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 290ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 259ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 301ms/epoch - 7ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 262ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 277ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 297ms/epoch - 7ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00486
45/45 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 00060: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704

WMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.57086226636761 
RMSE:	 8.400646538592587 
MAPE:	 6.6664001460728475

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 329.6035699397079 
RMSE:	 18.15498746735199 
MAPE:	 16.799244301034683

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 103.27437965196852 
RMSE:	 10.162400289890599 
MAPE:	 8.510636158449836
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.238, Time=3.61 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.578, Time=5.53 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16746.296, Time=8.58 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.578, Time=8.23 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16987.591, Time=3.89 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16395.520, Time=13.75 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17063.555, Time=13.49 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.578, Time=10.90 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16082.554, Time=21.32 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-15249.608, Time=19.15 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 108.468 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8563.778
Date:                Sun, 12 Dec 2021   AIC                         -17063.555
Time:                        17:49:18   BIC                         -16913.448
Sample:                             0   HQIC                        -17005.908
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.495e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x2         -1.485e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x3         -1.518e-10      0.000  -1.21e-06      1.000      -0.000       0.000
x4             1.0000      0.000   8075.329      0.000       1.000       1.000
x5         -1.356e-10      0.000  -1.15e-06      1.000      -0.000       0.000
x6         -2.861e-09      0.000  -2.38e-05      1.000      -0.000       0.000
x7         -1.374e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x8         -1.371e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x9         -7.133e-11    7.1e-06  -1.01e-05      1.000   -1.39e-05    1.39e-05
x10         -1.23e-10   4.21e-05  -2.92e-06      1.000   -8.24e-05    8.24e-05
x11        -1.357e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x12        -1.401e-10      0.000  -1.11e-06      1.000      -0.000       0.000
x13        -1.436e-10      0.000  -1.16e-06      1.000      -0.000       0.000
x14        -1.179e-09      0.000  -3.22e-06      1.000      -0.001       0.001
x15        -1.651e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x16        -1.064e-10      0.000  -9.62e-07      1.000      -0.000       0.000
x17        -1.041e-10      0.000  -9.53e-07      1.000      -0.000       0.000
x18        -4.477e-10      0.000  -1.99e-06      1.000      -0.000       0.000
x19        -1.816e-10      0.000  -1.26e-06      1.000      -0.000       0.000
x20         -4.37e-10      0.000  -1.96e-06      1.000      -0.000       0.000
x21        -1.371e-09    9.1e-05  -1.51e-05      1.000      -0.000       0.000
x22        -1.059e-11        nan        nan        nan         nan         nan
x23        -9.902e-11   3.83e-09     -0.026      0.979   -7.61e-09    7.41e-09
x24        -5.521e-09      0.000  -1.34e-05      1.000      -0.001       0.001
x25        -4.621e-09   6.42e-05   -7.2e-05      1.000      -0.000       0.000
x26        -1.587e-09      0.000  -3.73e-06      1.000      -0.001       0.001
x27        -8.504e-10      0.000  -2.79e-06      1.000      -0.001       0.001
x28        -1.122e-09      0.000  -3.14e-06      1.000      -0.001       0.001
x29        -6.091e-10      0.000  -2.45e-06      1.000      -0.000       0.000
ma.L1         -1.3318   7.32e-07  -1.82e+06      0.000      -1.332      -1.332
ma.L2          0.3767   7.56e-07   4.98e+05      0.000       0.377       0.377
sigma2      9.093e-11   6.97e-11      1.304      0.192   -4.57e-11    2.28e-10
===================================================================================
Ljung-Box (L1) (Q):                  76.00   Jarque-Bera (JB):            304933.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.03   Skew:                             1.65
Prob(H) (two-sided):                  0.00   Kurtosis:                        98.29
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.19e+28. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.03989, saving model to LSTM6.h5
58/58 - 4s - loss: 0.2600 - accuracy: 0.0000e+00 - val_loss: 0.0399 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 65ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.03989 to 0.00457, saving model to LSTM6.h5
58/58 - 0s - loss: 0.0229 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 0.0010 - 370ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0133 - accuracy: 0.0000e+00 - val_loss: 0.0159 - val_accuracy: 0.0037 - lr: 0.0010 - 353ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0097 - accuracy: 0.0000e+00 - val_loss: 0.0052 - val_accuracy: 0.0037 - lr: 0.0010 - 341ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0044 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 0.0010 - 343ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0107 - accuracy: 0.0000e+00 - val_loss: 0.0098 - val_accuracy: 0.0037 - lr: 0.0010 - 299ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0108 - accuracy: 0.0000e+00 - val_loss: 0.0140 - val_accuracy: 0.0037 - lr: 0.0010 - 310ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0247 - accuracy: 0.0000e+00 - val_loss: 0.0150 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 342ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0069 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 316ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0038 - accuracy: 0.0000e+00 - val_loss: 0.0096 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 325ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0029 - accuracy: 0.0000e+00 - val_loss: 0.0093 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 333ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00012: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0023 - accuracy: 0.0000e+00 - val_loss: 0.0090 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 314ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 305ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0083 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 337ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0079 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00017: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0078 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 343ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 345ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 325ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 331ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0074 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 321ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 341ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 334ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 330ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 347ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0068 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 345ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0067 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 343ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 337ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 309ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 353ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0064 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 342ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 357ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0062 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 312ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 318ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 331ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 350ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 334ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 345ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 320ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0055 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 311ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0055 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0054 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0054 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 421ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 362ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 331ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00457
58/58 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0052 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 368ms/epoch - 6ms/step
Epoch 00052: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704

WMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.57086226636761 
RMSE:	 8.400646538592587 
MAPE:	 6.6664001460728475

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 329.6035699397079 
RMSE:	 18.15498746735199 
MAPE:	 16.799244301034683

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 103.27437965196852 
RMSE:	 10.162400289890599 
MAPE:	 8.510636158449836

MIDPOINT
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 97.31838139819504 
RMSE:	 9.86500792692003 
MAPE:	 8.251875922025462
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16837.838, Time=3.68 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14497.319, Time=3.98 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16084.348, Time=6.86 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-15317.920, Time=12.01 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15304.480, Time=11.66 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15949.053, Time=12.77 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17059.707, Time=12.46 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-15313.920, Time=14.52 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16054.952, Time=13.57 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11445.350, Time=35.04 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 126.548 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8561.853
Date:                Sun, 12 Dec 2021   AIC                         -17059.707
Time:                        17:55:46   BIC                         -16909.600
Sample:                             0   HQIC                        -17002.059
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.003e-07   7.69e-05     -0.001      0.999      -0.000       0.000
x2         -1.001e-07   7.44e-05     -0.001      0.999      -0.000       0.000
x3         -1.006e-07   7.84e-05     -0.001      0.999      -0.000       0.000
x4             1.0000   7.11e-05   1.41e+04      0.000       1.000       1.000
x5         -9.611e-08   6.77e-05     -0.001      0.999      -0.000       0.000
x6         -1.249e-07   4.06e-05     -0.003      0.998   -7.96e-05    7.94e-05
x7             -1e-07   7.89e-05     -0.001      0.999      -0.000       0.000
x8            -0.0002   9.43e-05     -1.838      0.066      -0.000    1.15e-05
x9          2.853e-08   9.89e-05      0.000      1.000      -0.000       0.000
x10        -4.022e-05      0.000     -0.200      0.842      -0.000       0.000
x11            0.0003      7e-05      4.122      0.000       0.000       0.000
x12          7.55e-05      0.000      0.633      0.527      -0.000       0.000
x13        -1.005e-07   7.29e-05     -0.001      0.999      -0.000       0.000
x14        -2.756e-07      0.000     -0.001      0.999      -0.000       0.000
x15        -8.419e-08   8.98e-05     -0.001      0.999      -0.000       0.000
x16        -2.171e-07      0.000     -0.001      0.999      -0.000       0.000
x17        -1.105e-07   9.93e-05     -0.001      0.999      -0.000       0.000
x18         1.263e-07   3.22e-05      0.004      0.997   -6.31e-05    6.33e-05
x19        -8.769e-08      0.000     -0.001      0.999      -0.000       0.000
x20        -5.772e-08      0.000     -0.000      1.000      -0.000       0.000
x21         -9.77e-08      0.000     -0.001      1.000      -0.000       0.000
x22        -3.686e-12   7.09e-07   -5.2e-06      1.000   -1.39e-06    1.39e-06
x23        -9.216e-12    2.4e-05  -3.83e-07      1.000   -4.71e-05    4.71e-05
x24        -3.648e-07      0.000     -0.001      0.999      -0.001       0.001
x25        -1.391e-07      0.001     -0.000      1.000      -0.002       0.002
x26        -3.142e-07      0.000     -0.001      0.999      -0.001       0.001
x27        -3.042e-07   5.47e-05     -0.006      0.996      -0.000       0.000
x28        -1.785e-07      0.000     -0.001      0.999      -0.000       0.000
x29        -1.909e-07      0.000     -0.001      1.000      -0.001       0.001
ma.L1         -1.3901   8.24e-06  -1.69e+05      0.000      -1.390      -1.390
ma.L2          0.4035   2.01e-05   2.01e+04      0.000       0.403       0.404
sigma2      7.538e-11   6.94e-11      1.085      0.278   -6.07e-11    2.11e-10
===================================================================================
Ljung-Box (L1) (Q):                  69.36   Jarque-Bera (JB):           6470073.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                           -12.55
Prob(H) (two-sided):                  0.00   Kurtosis:                       441.48
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 3.58e+22. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.02365, saving model to LSTM6.h5
43/43 - 4s - loss: 0.1736 - accuracy: 0.0000e+00 - val_loss: 0.0236 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 87ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.02365 to 0.01039, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0498 - accuracy: 0.0000e+00 - val_loss: 0.0104 - val_accuracy: 0.0037 - lr: 0.0010 - 297ms/epoch - 7ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0484 - accuracy: 0.0000e+00 - val_loss: 0.0776 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 273ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0526 - accuracy: 0.0000e+00 - val_loss: 0.0138 - val_accuracy: 0.0037 - lr: 0.0010 - 299ms/epoch - 7ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0314 - accuracy: 0.0000e+00 - val_loss: 0.2012 - val_accuracy: 0.0000e+00 - lr: 0.0010 - 241ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0257 - accuracy: 0.0000e+00 - val_loss: 0.0410 - val_accuracy: 0.0037 - lr: 0.0010 - 272ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00007: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0164 - accuracy: 0.0000e+00 - val_loss: 0.0972 - val_accuracy: 0.0037 - lr: 0.0010 - 284ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0320 - accuracy: 0.0000e+00 - val_loss: 0.0406 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 240ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0042 - accuracy: 0.0000e+00 - val_loss: 0.0253 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 235ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0029 - accuracy: 0.0000e+00 - val_loss: 0.0174 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 304ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.01039
43/43 - 0s - loss: 0.0024 - accuracy: 0.0000e+00 - val_loss: 0.0119 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 251ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.01039 to 0.00836, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0020 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 295ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.00836 to 0.00619, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0018 - accuracy: 0.0000e+00 - val_loss: 0.0062 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 268ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.00619 to 0.00493, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0016 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 308ms/epoch - 7ms/step
Epoch 15/500

Epoch 00015: val_loss improved from 0.00493 to 0.00425, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0042 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 294ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.00425 to 0.00391, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 266ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.00391 to 0.00377, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0014 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 284ms/epoch - 7ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.00377 to 0.00372, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 279ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 229ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 276ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 276ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00022: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 262ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 7ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00027: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 260ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 260ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 262ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 239ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 282ms/epoch - 7ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00372
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss improved from 0.00372 to 0.00372, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss improved from 0.00372 to 0.00372, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 286ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss improved from 0.00372 to 0.00371, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss improved from 0.00371 to 0.00371, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss improved from 0.00371 to 0.00371, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss improved from 0.00371 to 0.00371, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 304ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss improved from 0.00371 to 0.00370, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 260ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss improved from 0.00370 to 0.00370, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 292ms/epoch - 7ms/step
Epoch 50/500

Epoch 00050: val_loss improved from 0.00370 to 0.00370, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 323ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss improved from 0.00370 to 0.00370, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 7ms/step
Epoch 52/500

Epoch 00052: val_loss improved from 0.00370 to 0.00369, saving model to LSTM6.h5
43/43 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 53/500

Epoch 00053: val_loss improved from 0.00369 to 0.00369, saving model to LSTM6.h5
43/43 - 0s - loss: 9.9916e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 290ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss improved from 0.00369 to 0.00369, saving model to LSTM6.h5
43/43 - 0s - loss: 9.9685e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 7ms/step
Epoch 55/500

Epoch 00055: val_loss improved from 0.00369 to 0.00368, saving model to LSTM6.h5
43/43 - 0s - loss: 9.9454e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss improved from 0.00368 to 0.00368, saving model to LSTM6.h5
43/43 - 0s - loss: 9.9221e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 289ms/epoch - 7ms/step
Epoch 57/500

Epoch 00057: val_loss improved from 0.00368 to 0.00368, saving model to LSTM6.h5
43/43 - 0s - loss: 9.8988e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 7ms/step
Epoch 58/500

Epoch 00058: val_loss improved from 0.00368 to 0.00367, saving model to LSTM6.h5
43/43 - 0s - loss: 9.8754e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss improved from 0.00367 to 0.00367, saving model to LSTM6.h5
43/43 - 0s - loss: 9.8519e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss improved from 0.00367 to 0.00367, saving model to LSTM6.h5
43/43 - 0s - loss: 9.8283e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 309ms/epoch - 7ms/step
Epoch 61/500

Epoch 00061: val_loss improved from 0.00367 to 0.00366, saving model to LSTM6.h5
43/43 - 0s - loss: 9.8046e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 7ms/step
Epoch 62/500

Epoch 00062: val_loss improved from 0.00366 to 0.00366, saving model to LSTM6.h5
43/43 - 0s - loss: 9.7808e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss improved from 0.00366 to 0.00366, saving model to LSTM6.h5
43/43 - 0s - loss: 9.7570e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 7ms/step
Epoch 64/500

Epoch 00064: val_loss improved from 0.00366 to 0.00365, saving model to LSTM6.h5
43/43 - 0s - loss: 9.7330e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 295ms/epoch - 7ms/step
Epoch 65/500

Epoch 00065: val_loss improved from 0.00365 to 0.00365, saving model to LSTM6.h5
43/43 - 0s - loss: 9.7090e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 66/500

Epoch 00066: val_loss improved from 0.00365 to 0.00365, saving model to LSTM6.h5
43/43 - 0s - loss: 9.6849e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss improved from 0.00365 to 0.00364, saving model to LSTM6.h5
43/43 - 0s - loss: 9.6607e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 300ms/epoch - 7ms/step
Epoch 68/500

Epoch 00068: val_loss improved from 0.00364 to 0.00364, saving model to LSTM6.h5
43/43 - 0s - loss: 9.6363e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 293ms/epoch - 7ms/step
Epoch 69/500

Epoch 00069: val_loss improved from 0.00364 to 0.00363, saving model to LSTM6.h5
43/43 - 0s - loss: 9.6119e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 7ms/step
Epoch 70/500

Epoch 00070: val_loss improved from 0.00363 to 0.00363, saving model to LSTM6.h5
43/43 - 0s - loss: 9.5874e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 291ms/epoch - 7ms/step
Epoch 71/500

Epoch 00071: val_loss improved from 0.00363 to 0.00362, saving model to LSTM6.h5
43/43 - 0s - loss: 9.5628e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 72/500

Epoch 00072: val_loss improved from 0.00362 to 0.00362, saving model to LSTM6.h5
43/43 - 0s - loss: 9.5381e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 73/500

Epoch 00073: val_loss improved from 0.00362 to 0.00361, saving model to LSTM6.h5
43/43 - 0s - loss: 9.5133e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 74/500

Epoch 00074: val_loss improved from 0.00361 to 0.00361, saving model to LSTM6.h5
43/43 - 0s - loss: 9.4884e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 302ms/epoch - 7ms/step
Epoch 75/500

Epoch 00075: val_loss improved from 0.00361 to 0.00360, saving model to LSTM6.h5
43/43 - 0s - loss: 9.4634e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 76/500

Epoch 00076: val_loss improved from 0.00360 to 0.00360, saving model to LSTM6.h5
43/43 - 0s - loss: 9.4382e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 7ms/step
Epoch 77/500

Epoch 00077: val_loss improved from 0.00360 to 0.00359, saving model to LSTM6.h5
43/43 - 0s - loss: 9.4130e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 78/500

Epoch 00078: val_loss improved from 0.00359 to 0.00358, saving model to LSTM6.h5
43/43 - 0s - loss: 9.3877e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss improved from 0.00358 to 0.00358, saving model to LSTM6.h5
43/43 - 0s - loss: 9.3623e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 282ms/epoch - 7ms/step
Epoch 80/500

Epoch 00080: val_loss improved from 0.00358 to 0.00357, saving model to LSTM6.h5
43/43 - 0s - loss: 9.3368e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 259ms/epoch - 6ms/step
Epoch 81/500

Epoch 00081: val_loss improved from 0.00357 to 0.00356, saving model to LSTM6.h5
43/43 - 0s - loss: 9.3112e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 300ms/epoch - 7ms/step
Epoch 82/500

Epoch 00082: val_loss improved from 0.00356 to 0.00356, saving model to LSTM6.h5
43/43 - 0s - loss: 9.2855e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 83/500

Epoch 00083: val_loss improved from 0.00356 to 0.00355, saving model to LSTM6.h5
43/43 - 0s - loss: 9.2597e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 84/500

Epoch 00084: val_loss improved from 0.00355 to 0.00354, saving model to LSTM6.h5
43/43 - 0s - loss: 9.2338e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 6ms/step
Epoch 85/500

Epoch 00085: val_loss improved from 0.00354 to 0.00353, saving model to LSTM6.h5
43/43 - 0s - loss: 9.2078e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 86/500

Epoch 00086: val_loss improved from 0.00353 to 0.00353, saving model to LSTM6.h5
43/43 - 0s - loss: 9.1817e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 87/500

Epoch 00087: val_loss improved from 0.00353 to 0.00352, saving model to LSTM6.h5
43/43 - 0s - loss: 9.1556e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 309ms/epoch - 7ms/step
Epoch 88/500

Epoch 00088: val_loss improved from 0.00352 to 0.00351, saving model to LSTM6.h5
43/43 - 0s - loss: 9.1294e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 299ms/epoch - 7ms/step
Epoch 89/500

Epoch 00089: val_loss improved from 0.00351 to 0.00350, saving model to LSTM6.h5
43/43 - 0s - loss: 9.1031e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 312ms/epoch - 7ms/step
Epoch 90/500

Epoch 00090: val_loss improved from 0.00350 to 0.00349, saving model to LSTM6.h5
43/43 - 0s - loss: 9.0767e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 7ms/step
Epoch 91/500

Epoch 00091: val_loss improved from 0.00349 to 0.00348, saving model to LSTM6.h5
43/43 - 0s - loss: 9.0503e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 300ms/epoch - 7ms/step
Epoch 92/500

Epoch 00092: val_loss improved from 0.00348 to 0.00347, saving model to LSTM6.h5
43/43 - 0s - loss: 9.0238e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 315ms/epoch - 7ms/step
Epoch 93/500

Epoch 00093: val_loss improved from 0.00347 to 0.00346, saving model to LSTM6.h5
43/43 - 0s - loss: 8.9973e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 262ms/epoch - 6ms/step
Epoch 94/500

Epoch 00094: val_loss improved from 0.00346 to 0.00346, saving model to LSTM6.h5
43/43 - 0s - loss: 8.9707e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 95/500

Epoch 00095: val_loss improved from 0.00346 to 0.00345, saving model to LSTM6.h5
43/43 - 0s - loss: 8.9441e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 308ms/epoch - 7ms/step
Epoch 96/500

Epoch 00096: val_loss improved from 0.00345 to 0.00344, saving model to LSTM6.h5
43/43 - 0s - loss: 8.9174e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 97/500

Epoch 00097: val_loss improved from 0.00344 to 0.00343, saving model to LSTM6.h5
43/43 - 0s - loss: 8.8907e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 98/500

Epoch 00098: val_loss improved from 0.00343 to 0.00342, saving model to LSTM6.h5
43/43 - 0s - loss: 8.8640e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 290ms/epoch - 7ms/step
Epoch 99/500

Epoch 00099: val_loss improved from 0.00342 to 0.00340, saving model to LSTM6.h5
43/43 - 0s - loss: 8.8373e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 281ms/epoch - 7ms/step
Epoch 100/500

Epoch 00100: val_loss improved from 0.00340 to 0.00339, saving model to LSTM6.h5
43/43 - 0s - loss: 8.8106e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 101/500

Epoch 00101: val_loss improved from 0.00339 to 0.00338, saving model to LSTM6.h5
43/43 - 0s - loss: 8.7839e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 283ms/epoch - 7ms/step
Epoch 102/500

Epoch 00102: val_loss improved from 0.00338 to 0.00337, saving model to LSTM6.h5
43/43 - 0s - loss: 8.7571e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 103/500

Epoch 00103: val_loss improved from 0.00337 to 0.00336, saving model to LSTM6.h5
43/43 - 0s - loss: 8.7304e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 7ms/step
Epoch 104/500

Epoch 00104: val_loss improved from 0.00336 to 0.00335, saving model to LSTM6.h5
43/43 - 0s - loss: 8.7037e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 275ms/epoch - 6ms/step
Epoch 105/500

Epoch 00105: val_loss improved from 0.00335 to 0.00334, saving model to LSTM6.h5
43/43 - 0s - loss: 8.6770e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 290ms/epoch - 7ms/step
Epoch 106/500

Epoch 00106: val_loss improved from 0.00334 to 0.00333, saving model to LSTM6.h5
43/43 - 0s - loss: 8.6504e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 107/500

Epoch 00107: val_loss improved from 0.00333 to 0.00332, saving model to LSTM6.h5
43/43 - 0s - loss: 8.6238e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 108/500

Epoch 00108: val_loss improved from 0.00332 to 0.00331, saving model to LSTM6.h5
43/43 - 0s - loss: 8.5973e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 109/500

Epoch 00109: val_loss improved from 0.00331 to 0.00330, saving model to LSTM6.h5
43/43 - 0s - loss: 8.5708e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 305ms/epoch - 7ms/step
Epoch 110/500

Epoch 00110: val_loss improved from 0.00330 to 0.00329, saving model to LSTM6.h5
43/43 - 0s - loss: 8.5443e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 283ms/epoch - 7ms/step
Epoch 111/500

Epoch 00111: val_loss improved from 0.00329 to 0.00327, saving model to LSTM6.h5
43/43 - 0s - loss: 8.5180e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 112/500

Epoch 00112: val_loss improved from 0.00327 to 0.00326, saving model to LSTM6.h5
43/43 - 0s - loss: 8.4917e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 113/500

Epoch 00113: val_loss improved from 0.00326 to 0.00325, saving model to LSTM6.h5
43/43 - 0s - loss: 8.4655e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 318ms/epoch - 7ms/step
Epoch 114/500

Epoch 00114: val_loss improved from 0.00325 to 0.00324, saving model to LSTM6.h5
43/43 - 0s - loss: 8.4393e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 7ms/step
Epoch 115/500

Epoch 00115: val_loss improved from 0.00324 to 0.00323, saving model to LSTM6.h5
43/43 - 0s - loss: 8.4133e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 116/500

Epoch 00116: val_loss improved from 0.00323 to 0.00322, saving model to LSTM6.h5
43/43 - 0s - loss: 8.3874e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 300ms/epoch - 7ms/step
Epoch 117/500

Epoch 00117: val_loss improved from 0.00322 to 0.00321, saving model to LSTM6.h5
43/43 - 0s - loss: 8.3616e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 118/500

Epoch 00118: val_loss improved from 0.00321 to 0.00320, saving model to LSTM6.h5
43/43 - 0s - loss: 8.3359e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 119/500

Epoch 00119: val_loss improved from 0.00320 to 0.00319, saving model to LSTM6.h5
43/43 - 0s - loss: 8.3103e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 120/500

Epoch 00120: val_loss improved from 0.00319 to 0.00319, saving model to LSTM6.h5
43/43 - 0s - loss: 8.2848e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 260ms/epoch - 6ms/step
Epoch 121/500

Epoch 00121: val_loss improved from 0.00319 to 0.00318, saving model to LSTM6.h5
43/43 - 0s - loss: 8.2595e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 122/500

Epoch 00122: val_loss improved from 0.00318 to 0.00317, saving model to LSTM6.h5
43/43 - 0s - loss: 8.2343e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 277ms/epoch - 6ms/step
Epoch 123/500

Epoch 00123: val_loss improved from 0.00317 to 0.00316, saving model to LSTM6.h5
43/43 - 0s - loss: 8.2093e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 7ms/step
Epoch 124/500

Epoch 00124: val_loss improved from 0.00316 to 0.00315, saving model to LSTM6.h5
43/43 - 0s - loss: 8.1844e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 279ms/epoch - 6ms/step
Epoch 125/500

Epoch 00125: val_loss improved from 0.00315 to 0.00315, saving model to LSTM6.h5
43/43 - 0s - loss: 8.1596e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 300ms/epoch - 7ms/step
Epoch 126/500

Epoch 00126: val_loss improved from 0.00315 to 0.00314, saving model to LSTM6.h5
43/43 - 0s - loss: 8.1350e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 127/500

Epoch 00127: val_loss improved from 0.00314 to 0.00313, saving model to LSTM6.h5
43/43 - 0s - loss: 8.1106e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 317ms/epoch - 7ms/step
Epoch 128/500

Epoch 00128: val_loss improved from 0.00313 to 0.00313, saving model to LSTM6.h5
43/43 - 0s - loss: 8.0864e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 129/500

Epoch 00129: val_loss improved from 0.00313 to 0.00312, saving model to LSTM6.h5
43/43 - 0s - loss: 8.0623e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 130/500

Epoch 00130: val_loss improved from 0.00312 to 0.00312, saving model to LSTM6.h5
43/43 - 0s - loss: 8.0383e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 286ms/epoch - 7ms/step
Epoch 131/500

Epoch 00131: val_loss improved from 0.00312 to 0.00312, saving model to LSTM6.h5
43/43 - 0s - loss: 8.0146e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 285ms/epoch - 7ms/step
Epoch 132/500

Epoch 00132: val_loss improved from 0.00312 to 0.00311, saving model to LSTM6.h5
43/43 - 0s - loss: 7.9910e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 133/500

Epoch 00133: val_loss improved from 0.00311 to 0.00311, saving model to LSTM6.h5
43/43 - 0s - loss: 7.9676e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 284ms/epoch - 7ms/step
Epoch 134/500

Epoch 00134: val_loss improved from 0.00311 to 0.00311, saving model to LSTM6.h5
43/43 - 0s - loss: 7.9444e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 135/500

Epoch 00135: val_loss improved from 0.00311 to 0.00311, saving model to LSTM6.h5
43/43 - 0s - loss: 7.9214e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 136/500

Epoch 00136: val_loss improved from 0.00311 to 0.00311, saving model to LSTM6.h5
43/43 - 0s - loss: 7.8986e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 137/500

Epoch 00137: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.8760e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 138/500

Epoch 00138: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.8535e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 139/500

Epoch 00139: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.8313e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 140/500

Epoch 00140: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.8092e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 141/500

Epoch 00141: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.7874e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 291ms/epoch - 7ms/step
Epoch 142/500

Epoch 00142: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.7657e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 143/500

Epoch 00143: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.7443e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 144/500

Epoch 00144: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.7230e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 302ms/epoch - 7ms/step
Epoch 145/500

Epoch 00145: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.7020e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 146/500

Epoch 00146: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.6811e-04 - accuracy: 0.0000e+00 - val_loss: 0.0031 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 147/500

Epoch 00147: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.6605e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 148/500

Epoch 00148: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.6400e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 149/500

Epoch 00149: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.6198e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 255ms/epoch - 6ms/step
Epoch 150/500

Epoch 00150: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.5997e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 151/500

Epoch 00151: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.5799e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 240ms/epoch - 6ms/step
Epoch 152/500

Epoch 00152: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.5602e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 276ms/epoch - 6ms/step
Epoch 153/500

Epoch 00153: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.5408e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 154/500

Epoch 00154: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.5215e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 155/500

Epoch 00155: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.5024e-04 - accuracy: 0.0000e+00 - val_loss: 0.0032 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 156/500

Epoch 00156: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.4836e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 157/500

Epoch 00157: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.4649e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 158/500

Epoch 00158: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.4464e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 159/500

Epoch 00159: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.4280e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 245ms/epoch - 6ms/step
Epoch 160/500

Epoch 00160: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.4099e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 282ms/epoch - 7ms/step
Epoch 161/500

Epoch 00161: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.3919e-04 - accuracy: 0.0000e+00 - val_loss: 0.0033 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 162/500

Epoch 00162: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.3741e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 163/500

Epoch 00163: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.3565e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 283ms/epoch - 7ms/step
Epoch 164/500

Epoch 00164: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.3391e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 258ms/epoch - 6ms/step
Epoch 165/500

Epoch 00165: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.3218e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 166/500

Epoch 00166: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.3047e-04 - accuracy: 0.0000e+00 - val_loss: 0.0034 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 167/500

Epoch 00167: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.2878e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 168/500

Epoch 00168: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.2710e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 277ms/epoch - 6ms/step
Epoch 169/500

Epoch 00169: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.2543e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 170/500

Epoch 00170: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.2379e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 242ms/epoch - 6ms/step
Epoch 171/500

Epoch 00171: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.2215e-04 - accuracy: 0.0000e+00 - val_loss: 0.0035 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 172/500

Epoch 00172: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.2054e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 173/500

Epoch 00173: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.1893e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 174/500

Epoch 00174: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.1734e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 175/500

Epoch 00175: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.1577e-04 - accuracy: 0.0000e+00 - val_loss: 0.0036 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 176/500

Epoch 00176: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.1421e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 177/500

Epoch 00177: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.1266e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 259ms/epoch - 6ms/step
Epoch 178/500

Epoch 00178: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.1112e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 280ms/epoch - 7ms/step
Epoch 179/500

Epoch 00179: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0960e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 294ms/epoch - 7ms/step
Epoch 180/500

Epoch 00180: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0809e-04 - accuracy: 0.0000e+00 - val_loss: 0.0037 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 181/500

Epoch 00181: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0659e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 182/500

Epoch 00182: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0510e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 183/500

Epoch 00183: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0363e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 184/500

Epoch 00184: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0216e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 185/500

Epoch 00185: val_loss did not improve from 0.00311
43/43 - 0s - loss: 7.0071e-04 - accuracy: 0.0000e+00 - val_loss: 0.0038 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 186/500

Epoch 00186: val_loss did not improve from 0.00311
43/43 - 0s - loss: 6.9927e-04 - accuracy: 0.0000e+00 - val_loss: 0.0039 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 303ms/epoch - 7ms/step
Epoch 00186: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704

WMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.57086226636761 
RMSE:	 8.400646538592587 
MAPE:	 6.6664001460728475

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 329.6035699397079 
RMSE:	 18.15498746735199 
MAPE:	 16.799244301034683

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 103.27437965196852 
RMSE:	 10.162400289890599 
MAPE:	 8.510636158449836

MIDPOINT
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 97.31838139819504 
RMSE:	 9.86500792692003 
MAPE:	 8.251875922025462

T3
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 154.17386959926716 
RMSE:	 12.41667707558134 
MAPE:	 10.12780101556255
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16736.686, Time=3.84 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-15327.143, Time=3.43 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15166.078, Time=7.53 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14962.662, Time=14.44 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16731.606, Time=6.04 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14848.952, Time=10.31 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16921.745, Time=6.21 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14958.662, Time=18.13 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15003.046, Time=13.56 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-16752.122, Time=3.90 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 87.410 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8492.873
Date:                Sun, 12 Dec 2021   AIC                         -16921.745
Time:                        18:02:15   BIC                         -16771.638
Sample:                             0   HQIC                        -16864.098
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          2.277e-08      0.001   3.25e-05      1.000      -0.001       0.001
x2          2.286e-08      0.001    2.5e-05      1.000      -0.002       0.002
x3          2.286e-08      0.001   3.44e-05      1.000      -0.001       0.001
x4             1.0000      0.000   3190.279      0.000       0.999       1.001
x5          2.174e-08      0.001   4.21e-05      1.000      -0.001       0.001
x6          6.124e-09   3.05e-05      0.000      1.000   -5.97e-05    5.97e-05
x7          2.246e-08      0.001   1.67e-05      1.000      -0.003       0.003
x8            -0.0013      0.001     -1.669      0.095      -0.003       0.000
x9         -5.239e-09      0.000  -1.79e-05      1.000      -0.001       0.001
x10            0.0001    9.9e-05      1.396      0.163   -5.59e-05       0.000
x11           -0.0001      0.001     -0.177      0.859      -0.002       0.001
x12            0.0012      0.001      1.426      0.154      -0.000       0.003
x13         2.284e-08      0.000   6.75e-05      1.000      -0.001       0.001
x14         6.258e-08      0.001   5.07e-05      1.000      -0.002       0.002
x15         2.215e-08      0.000      0.000      1.000      -0.000       0.000
x16         3.243e-08      0.000      0.000      1.000      -0.001       0.001
x17          2.22e-08      0.000      0.000      1.000      -0.000       0.000
x18         7.527e-09      0.000   1.67e-05      1.000      -0.001       0.001
x19         2.477e-08      0.000      0.000      1.000      -0.000       0.000
x20        -2.348e-08      0.000  -5.78e-05      1.000      -0.001       0.001
x21         2.718e-08    5.8e-05      0.000      1.000      -0.000       0.000
x22        -2.176e-10      0.000  -5.27e-07      1.000      -0.001       0.001
x23         -2.69e-09   8.49e-05  -3.17e-05      1.000      -0.000       0.000
x24        -4.516e-08   7.24e-06     -0.006      0.995   -1.42e-05    1.41e-05
x25        -4.213e-08   2.81e-05     -0.002      0.999   -5.51e-05     5.5e-05
x26         7.946e-08      0.001      0.000      1.000      -0.001       0.001
x27         4.528e-08      0.001   6.21e-05      1.000      -0.001       0.001
x28          5.92e-08      0.001   4.12e-05      1.000      -0.003       0.003
x29         3.468e-08      0.000   7.06e-05      1.000      -0.001       0.001
ma.L1         -1.3739   4.46e-06  -3.08e+05      0.000      -1.374      -1.374
ma.L2          0.3968    1.4e-05   2.84e+04      0.000       0.397       0.397
sigma2      7.701e-11   7.39e-11      1.043      0.297   -6.78e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  61.47   Jarque-Bera (JB):           5565463.09
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                            10.97
Prob(H) (two-sided):                  0.00   Kurtosis:                       409.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 6.67e+20. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.09728, saving model to LSTM6.h5
90/90 - 4s - loss: 0.1306 - accuracy: 0.0000e+00 - val_loss: 0.0973 - val_accuracy: 0.0037 - lr: 0.0010 - 4s/epoch - 48ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.09728 to 0.01880, saving model to LSTM6.h5
90/90 - 1s - loss: 0.1590 - accuracy: 0.0000e+00 - val_loss: 0.0188 - val_accuracy: 0.0037 - lr: 0.0010 - 513ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.01880
90/90 - 1s - loss: 0.0262 - accuracy: 0.0000e+00 - val_loss: 0.0237 - val_accuracy: 0.0037 - lr: 0.0010 - 514ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01880
90/90 - 0s - loss: 0.0257 - accuracy: 0.0000e+00 - val_loss: 0.0481 - val_accuracy: 0.0037 - lr: 0.0010 - 479ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.01880 to 0.01832, saving model to LSTM6.h5
90/90 - 1s - loss: 0.0182 - accuracy: 0.0000e+00 - val_loss: 0.0183 - val_accuracy: 0.0037 - lr: 0.0010 - 566ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01832
90/90 - 0s - loss: 0.0211 - accuracy: 0.0000e+00 - val_loss: 0.0198 - val_accuracy: 0.0037 - lr: 0.0010 - 484ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.01832 to 0.00854, saving model to LSTM6.h5
90/90 - 1s - loss: 0.0149 - accuracy: 0.0000e+00 - val_loss: 0.0085 - val_accuracy: 0.0037 - lr: 0.0010 - 535ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.00854
90/90 - 0s - loss: 0.0148 - accuracy: 0.0000e+00 - val_loss: 0.0155 - val_accuracy: 0.0037 - lr: 0.0010 - 468ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.00854
90/90 - 1s - loss: 0.0132 - accuracy: 0.0000e+00 - val_loss: 0.0116 - val_accuracy: 0.0037 - lr: 0.0010 - 578ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.00854
90/90 - 0s - loss: 0.0128 - accuracy: 0.0000e+00 - val_loss: 0.0147 - val_accuracy: 0.0037 - lr: 0.0010 - 460ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.00854
90/90 - 1s - loss: 0.0124 - accuracy: 0.0000e+00 - val_loss: 0.0126 - val_accuracy: 0.0037 - lr: 0.0010 - 532ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00012: val_loss did not improve from 0.00854
90/90 - 1s - loss: 0.0122 - accuracy: 0.0000e+00 - val_loss: 0.0144 - val_accuracy: 0.0037 - lr: 0.0010 - 529ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.00854
90/90 - 1s - loss: 0.0208 - accuracy: 0.0000e+00 - val_loss: 0.0251 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 510ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.00854
90/90 - 0s - loss: 0.0129 - accuracy: 0.0000e+00 - val_loss: 0.0136 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 479ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.00854
90/90 - 1s - loss: 0.0091 - accuracy: 0.0000e+00 - val_loss: 0.0094 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 525ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.00854 to 0.00725, saving model to LSTM6.h5
90/90 - 1s - loss: 0.0070 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 577ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.00725 to 0.00605, saving model to LSTM6.h5
90/90 - 0s - loss: 0.0055 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 496ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.00605 to 0.00532, saving model to LSTM6.h5
90/90 - 1s - loss: 0.0044 - accuracy: 0.0000e+00 - val_loss: 0.0053 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 516ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.00532 to 0.00489, saving model to LSTM6.h5
90/90 - 1s - loss: 0.0036 - accuracy: 0.0000e+00 - val_loss: 0.0049 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 565ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss improved from 0.00489 to 0.00464, saving model to LSTM6.h5
90/90 - 1s - loss: 0.0030 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 515ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss improved from 0.00464 to 0.00456, saving model to LSTM6.h5
90/90 - 0s - loss: 0.0025 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 462ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0022 - accuracy: 0.0000e+00 - val_loss: 0.0046 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 486ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0019 - accuracy: 0.0000e+00 - val_loss: 0.0048 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 454ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0017 - accuracy: 0.0000e+00 - val_loss: 0.0050 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 468ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00025: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0015 - accuracy: 0.0000e+00 - val_loss: 0.0054 - val_accuracy: 0.0037 - lr: 1.0000e-04 - 467ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0013 - accuracy: 0.0000e+00 - val_loss: 0.0056 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 545ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0057 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 484ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0058 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 462ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0059 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 495ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00030: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0060 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 510ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0061 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 552ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0062 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 510ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0063 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 493ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0064 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 503ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0012 - accuracy: 0.0000e+00 - val_loss: 0.0065 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 477ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0066 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 546ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0067 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 560ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0068 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 532ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0069 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 478ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0070 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 493ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0071 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 456ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0072 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 504ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0073 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 560ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0075 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 592ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0076 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 550ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0011 - accuracy: 0.0000e+00 - val_loss: 0.0077 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 607ms/epoch - 7ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0078 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 468ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0079 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 480ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0081 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 468ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0082 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 498ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.00456
90/90 - 1s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0083 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 552ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00456
90/90 - 0s - loss: 0.0010 - accuracy: 0.0000e+00 - val_loss: 0.0084 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 483ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.9579e-04 - accuracy: 0.0000e+00 - val_loss: 0.0086 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 470ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.8898e-04 - accuracy: 0.0000e+00 - val_loss: 0.0087 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 550ms/epoch - 6ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.8242e-04 - accuracy: 0.0000e+00 - val_loss: 0.0088 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 470ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.7611e-04 - accuracy: 0.0000e+00 - val_loss: 0.0090 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 460ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.7005e-04 - accuracy: 0.0000e+00 - val_loss: 0.0091 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 540ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.6423e-04 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 493ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.5863e-04 - accuracy: 0.0000e+00 - val_loss: 0.0094 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 466ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.5326e-04 - accuracy: 0.0000e+00 - val_loss: 0.0095 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 476ms/epoch - 5ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.4809e-04 - accuracy: 0.0000e+00 - val_loss: 0.0097 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 479ms/epoch - 5ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.4313e-04 - accuracy: 0.0000e+00 - val_loss: 0.0098 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 475ms/epoch - 5ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.3834e-04 - accuracy: 0.0000e+00 - val_loss: 0.0100 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 545ms/epoch - 6ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.3372e-04 - accuracy: 0.0000e+00 - val_loss: 0.0101 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 484ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.2925e-04 - accuracy: 0.0000e+00 - val_loss: 0.0102 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 571ms/epoch - 6ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.2492e-04 - accuracy: 0.0000e+00 - val_loss: 0.0104 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 464ms/epoch - 5ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.2071e-04 - accuracy: 0.0000e+00 - val_loss: 0.0105 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 535ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.1660e-04 - accuracy: 0.0000e+00 - val_loss: 0.0107 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 483ms/epoch - 5ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.1259e-04 - accuracy: 0.0000e+00 - val_loss: 0.0108 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 541ms/epoch - 6ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.00456
90/90 - 1s - loss: 9.0865e-04 - accuracy: 0.0000e+00 - val_loss: 0.0110 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 539ms/epoch - 6ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.00456
90/90 - 0s - loss: 9.0479e-04 - accuracy: 0.0000e+00 - val_loss: 0.0111 - val_accuracy: 0.0037 - lr: 1.0000e-05 - 482ms/epoch - 5ms/step
Epoch 00071: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 75.03401716737034 
RMSE:	 8.662217797271685 
MAPE:	 7.077228582293258

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.28436187942754 
RMSE:	 8.383576914386099 
MAPE:	 6.876111393338704

WMA
Prediction vs Close:		54.85% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 70.57086226636761 
RMSE:	 8.400646538592587 
MAPE:	 6.6664001460728475

DEMA
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 329.6035699397079 
RMSE:	 18.15498746735199 
MAPE:	 16.799244301034683

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 103.27437965196852 
RMSE:	 10.162400289890599 
MAPE:	 8.510636158449836

MIDPOINT
Prediction vs Close:		51.87% Accuracy
Prediction vs Prediction:	45.15% Accuracy
MSE:	 97.31838139819504 
RMSE:	 9.86500792692003 
MAPE:	 8.251875922025462

T3
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	46.27% Accuracy
MSE:	 154.17386959926716 
RMSE:	 12.41667707558134 
MAPE:	 10.12780101556255

TEMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	48.51% Accuracy
MSE:	 87.38626729945001 
RMSE:	 9.348062221629144 
MAPE:	 8.358174186376312
Runtime: mins: 58.01520771974999

Architecture Used

In [124]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving Experiment6.png to Experiment6 (1).png
In [125]:
img = cv2.imread('Experiment6.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[125]:
<matplotlib.image.AxesImage at 0x7fa5cfcc6b10>

Model Plots

In [126]:
for i in range(len(list(simulation6.keys()))):
  SIM = list(simulation6.keys())[i]
  plot_train(simulation6,SIM)
  plot_test(simulation6,SIM)
----- Train RMSE for SMA ----- 8.882238711112084
----- Train_MSE_LSTM for SMA ----- 78.89416452117804
----- Train MAE LSTM for SMA ----- 7.762640273567326
----- Test RMSE for SMA----- 8.662217797271685
----- Test_MSE_LSTM for SMA----- 75.03401716737034
----- Test_MAE_LSTM for SMA----- 7.077228582293258
----- Train RMSE for EMA ----- 10.160180455912146
----- Train_MSE_LSTM for EMA ----- 103.22926689669913
----- Train MAE LSTM for EMA ----- 9.005262034617845
----- Test RMSE for EMA----- 8.383576914386099
----- Test_MSE_LSTM for EMA----- 70.28436187942754
----- Test_MAE_LSTM for EMA----- 6.876111393338704
----- Train RMSE for WMA ----- 10.433402093205265
----- Train_MSE_LSTM for WMA ----- 108.85587923850001
----- Train MAE LSTM for WMA ----- 9.27337175169683
----- Test RMSE for WMA----- 8.400646538592587
----- Test_MSE_LSTM for WMA----- 70.57086226636761
----- Test_MAE_LSTM for WMA----- 6.6664001460728475
----- Train RMSE for DEMA ----- 12.041548116714113
----- Train_MSE_LSTM for DEMA ----- 144.9988810471412
----- Train MAE LSTM for DEMA ----- 10.79869721264182
----- Test RMSE for DEMA----- 18.15498746735199
----- Test_MSE_LSTM for DEMA----- 329.6035699397079
----- Test_MAE_LSTM for DEMA----- 16.799244301034683
----- Train RMSE for KAMA ----- 10.551998588436238
----- Train_MSE_LSTM for KAMA ----- 111.34467421036034
----- Train MAE LSTM for KAMA ----- 9.496739060524044
----- Test RMSE for KAMA----- 10.162400289890599
----- Test_MSE_LSTM for KAMA----- 103.27437965196852
----- Test_MAE_LSTM for KAMA----- 8.510636158449836
----- Train RMSE for MIDPOINT ----- 9.500193980286314
----- Train_MSE_LSTM for MIDPOINT ----- 90.25368566306832
----- Train MAE LSTM for MIDPOINT ----- 8.42568593891395
----- Test RMSE for MIDPOINT----- 9.86500792692003
----- Test_MSE_LSTM for MIDPOINT----- 97.31838139819504
----- Test_MAE_LSTM for MIDPOINT----- 8.251875922025462
----- Train RMSE for T3 ----- 12.076038211073818
----- Train_MSE_LSTM for T3 ----- 145.83069887531494
----- Train MAE LSTM for T3 ----- 10.880763551400108
----- Test RMSE for T3----- 12.41667707558134
----- Test_MSE_LSTM for T3----- 154.17386959926716
----- Test_MAE_LSTM for T3----- 10.12780101556255
----- Train RMSE for TEMA ----- 7.43933113042144
----- Train_MSE_LSTM for TEMA ----- 55.343647668057535
----- Train MAE LSTM for TEMA ----- 5.114719427400499
----- Test RMSE for TEMA----- 9.348062221629144
----- Test_MSE_LSTM for TEMA----- 87.38626729945001
----- Test_MAE_LSTM for TEMA----- 8.358174186376312

Arima w Exogenous Variable Multistep MutiVariate LSTM Hybrid Model Experiment 7

In [127]:
def get_arima_exog(dataframe,original_data, train_len, test_len):    
    

    # prepare train and test data for exogenous vr
    X_value = pd.DataFrame(low_vol.iloc[:, :])
    y_value = pd.DataFrame(low_vol.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    X_scale_dataset = X_scaler.fit_transform(X_value)
    y_scale_dataset = y_scaler.fit_transform(y_value)
    # Get data and check shape
    # X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X_scale_dataset)
    y_train, y_test, = split_train_test(y_scale_dataset)
    yc_train,yc_test = split_train_test(low_vol_data)
    yc = yc_test.values.tolist()
    y_train_list = y_train.flatten().tolist()
    y_test_list = y_test.flatten().tolist()
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)

    # Initialize model
    model = auto_arima(y_train_list,exogenous  = X_train,trace=True, error_action='ignore', start_p=1,start_q=1,max_p=3,max_q=3,d=3,
            suppress_warnings=True,stepwise=True,seasonal=True)

      # Determine model parameters
    print(model.summary())
    model.fit(y_train_list,maxiter=200)
    order = model.get_params()['order']
    print('ARIMA order:', order, '\n')

      # Genereate predictions
    prediction = []
    for i in range(len(y_test_list)):
        model = pmdarima.ARIMA(order=order)
        model.fit(y_train_list)
        # print('working on', i+1, 'of', len(y_test), '-- ' + str(int(100 * (i + 1) / len(y_test))) + '% complete')

        prediction.append(model.predict()[0])
        y_train_list.append(y_test_list[i])

    predictionte = y_scaler.inverse_transform(np.array(prediction).reshape(-1,1))
    y_test_ = y_scaler.inverse_transform(np.array(y_test_list).reshape(-1,1))

    # Generate error data
    mse = mean_squared_error(yc_test, predictionte)
    rmse = mse ** 0.5
    mae = mean_absolute_error(y_test_ , predictionte )
    return yc,predictionte.flatten().tolist(), mse, rmse, mae
In [128]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det =20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # Option 1
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    # model.add(Dense(units=64,activation='relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    # ## Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()


    # # # option 2
    # model = Sequential()
    # model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    # model.add(Dense(64))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM7.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma+' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    class Double_Tanh(Activation):
        def __init__(self, activation, **kwargs):
            super(Double_Tanh, self).__init__(activation, **kwargs)
            self.__name__ = 'double_tanh'

    def double_tanh(x):
        return (K.tanh(x) * 2)

    get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
        # Model Generation
    model = Sequential()
    #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    model.add(Dense(1))
    model.add(Activation(double_tanh))
    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM7.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()

    # Option 4
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(x_train.shape[1], 1)))
    # model.add(LSTM(units=int(lstm_len/2)))
    # model.add(Dense(1, activation='sigmoid'))
    # model.compile(loss='mean_squared_error', optimizer='adam')
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [129]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation7 = {}
    imgfile = 'Experiment7'
    for ma in optimized_period:
                print(ma)
                print(functions[ma])
                print ( int( optimized_period[ma]))
              # if ma == 'SMA':
                low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
                low_vol = low_vol.fillna(0)
                low_vol_data = df['close']
                high_vol = pd.DataFrame()
                df2 = df.copy()
                for i in df2.columns:
                  if i in low_vol.columns:
                    high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
                high_vol_data = df['close']
                ## *****************************************************
                # Generate ARIMA and LSTM predictions
                print('\nWorking on ' + ma + ' predictions')
                try:
                  print('parameters used : ', train_len, test_len)
                  low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima_exog(low_vol,low_vol_data, train_len, test_len)
                except:
                    print('ARIMA error, skipping to next MA type')
                    continue
                Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
                final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
                mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
                rmse_ftr = mse_ftr ** 0.5
                mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
                mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

                final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
                mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
                rmse = mse ** 0.5
                mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                # Generate prediction accuracy
                actual = df['close'].tail(test_len).values
                result_1 = []
                result_2 = []
                for i in range(1, len(final_prediction)):
                    # Compare prediction to previous close price
                    if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                        result_1.append(1)
                    elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                        result_1.append(1)
                    else:
                        result_1.append(0)

                    # Compare prediction to previous prediction
                    if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                        result_2.append(1)
                    elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                        result_2.append(1)
                    else:
                        result_2.append(0)

                accuracy_1 = np.mean(result_1)
                accuracy_2 = np.mean(result_2)

                simulation7[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                              'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                  'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                              'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                  'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                              'rmse': rmse_ftr, 'mae' : mae_ftr},
                                  'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                            'rmse': rmse, 'mae': mae },
                                  'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

                # save simulation data here as checkpoint
                with open('simulation7_data.json', 'w') as fp:
                    json.dump(simulation7, fp)

                for ma in simulation7.keys():
                    print('\n' + ma)
                    print('Prediction vs Close:\t\t' + str(round(100*simulation7[ma]['accuracy']['prediction vs close'], 2))
                          + '% Accuracy')
                    print('Prediction vs Prediction:\t' + str(round(100*simulation7[ma]['accuracy']['prediction vs prediction'], 2))
                          + '% Accuracy')
                    print('MSE:\t', simulation7[ma]['final']['mse'],
                          '\nRMSE:\t', simulation7[ma]['final']['rmse'],
                          '\nMAPE:\t', simulation7[ma]['final']['mae'])#,
                          # '\nMAPE:\t', simulation[ma]['final']['mape'])
              # else:
              #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.787, Time=3.58 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.588, Time=5.63 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14596.280, Time=5.74 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.588, Time=8.45 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16924.805, Time=10.66 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14482.349, Time=11.42 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17215.608, Time=20.46 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.588, Time=11.13 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15570.350, Time=19.72 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11671.292, Time=28.32 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 125.145 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8639.804
Date:                Sun, 12 Dec 2021   AIC                         -17215.608
Time:                        18:11:46   BIC                         -17065.501
Sample:                             0   HQIC                        -17157.961
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -4.057e-09   5.82e-05  -6.97e-05      1.000      -0.000       0.000
x2         -4.057e-09   5.81e-05  -6.99e-05      1.000      -0.000       0.000
x3         -4.111e-09   5.49e-05  -7.49e-05      1.000      -0.000       0.000
x4             1.0000   5.71e-05   1.75e+04      0.000       1.000       1.000
x5         -3.706e-09   5.43e-05  -6.82e-05      1.000      -0.000       0.000
x6         -1.082e-08      0.000  -6.08e-05      1.000      -0.000       0.000
x7         -4.025e-09   5.63e-05  -7.15e-05      1.000      -0.000       0.000
x8         -4.035e-09   5.19e-05  -7.78e-05      1.000      -0.000       0.000
x9         -1.522e-10    2.9e-05  -5.25e-06      1.000   -5.68e-05    5.68e-05
x10        -6.396e-10   1.04e-05  -6.15e-05      1.000   -2.04e-05    2.04e-05
x11        -3.921e-09   5.06e-05  -7.75e-05      1.000   -9.91e-05    9.91e-05
x12        -4.102e-09   5.29e-05  -7.76e-05      1.000      -0.000       0.000
x13        -4.087e-09   5.75e-05  -7.11e-05      1.000      -0.000       0.000
x14        -3.619e-08      0.000     -0.000      1.000      -0.000       0.000
x15        -4.806e-09   4.61e-05     -0.000      1.000   -9.03e-05    9.03e-05
x16        -3.507e-09      0.000  -2.98e-05      1.000      -0.000       0.000
x17        -3.121e-09   6.02e-05  -5.18e-05      1.000      -0.000       0.000
x18        -1.172e-08      0.000     -0.000      1.000      -0.000       0.000
x19        -5.433e-09   6.06e-05  -8.96e-05      1.000      -0.000       0.000
x20        -1.393e-08   4.79e-05     -0.000      1.000   -9.39e-05    9.39e-05
x21        -4.216e-09   6.63e-05  -6.36e-05      1.000      -0.000       0.000
x22        -3.479e-11   1.66e-08     -0.002      0.998   -3.25e-08    3.24e-08
x23        -9.221e-10    1.4e-07     -0.007      0.995   -2.74e-07    2.73e-07
x24        -8.085e-08      0.001  -6.96e-05      1.000      -0.002       0.002
x25        -9.642e-08      0.001     -0.000      1.000      -0.002       0.002
x26        -5.019e-08      0.000     -0.000      1.000      -0.000       0.000
x27        -2.457e-08   7.65e-05     -0.000      1.000      -0.000       0.000
x28        -3.411e-08      0.000     -0.000      1.000      -0.000       0.000
x29        -1.507e-08   4.36e-05     -0.000      1.000   -8.54e-05    8.54e-05
ma.L1         -1.3898   8.03e-07  -1.73e+06      0.000      -1.390      -1.390
ma.L2          0.4031   8.36e-07   4.82e+05      0.000       0.403       0.403
sigma2      7.528e-11   7.24e-11      1.040      0.298   -6.66e-11    2.17e-10
===================================================================================
Ljung-Box (L1) (Q):                  89.12   Jarque-Bera (JB):           1533103.33
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             5.56
Prob(H) (two-sided):                  0.00   Kurtosis:                       216.50
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.08e+25. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.32789, saving model to LSTM7.h5
48/48 - 2s - loss: 0.0877 - mse: 0.0877 - mae: 0.2289 - val_loss: 0.3279 - val_mse: 0.3279 - val_mae: 0.5464 - lr: 0.0010 - 2s/epoch - 44ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.32789 to 0.08109, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0258 - mse: 0.0258 - mae: 0.1276 - val_loss: 0.0811 - val_mse: 0.0811 - val_mae: 0.2555 - lr: 0.0010 - 243ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.08109
48/48 - 0s - loss: 0.0165 - mse: 0.0165 - mae: 0.1010 - val_loss: 0.0825 - val_mse: 0.0825 - val_mae: 0.2611 - lr: 0.0010 - 200ms/epoch - 4ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.08109 to 0.06805, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0124 - mse: 0.0124 - mae: 0.0885 - val_loss: 0.0680 - val_mse: 0.0680 - val_mae: 0.2354 - lr: 0.0010 - 238ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.06805
48/48 - 0s - loss: 0.0099 - mse: 0.0099 - mae: 0.0771 - val_loss: 0.0897 - val_mse: 0.0897 - val_mae: 0.2766 - lr: 0.0010 - 208ms/epoch - 4ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.06805 to 0.04796, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0111 - mse: 0.0111 - mae: 0.0810 - val_loss: 0.0480 - val_mse: 0.0480 - val_mae: 0.1935 - lr: 0.0010 - 232ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04796
48/48 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0693 - val_loss: 0.0706 - val_mse: 0.0706 - val_mae: 0.2443 - lr: 0.0010 - 252ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.04796 to 0.03656, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0701 - val_loss: 0.0366 - val_mse: 0.0366 - val_mae: 0.1669 - lr: 0.0010 - 286ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0662 - val_loss: 0.0638 - val_mse: 0.0638 - val_mae: 0.2318 - lr: 0.0010 - 261ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0694 - val_loss: 0.0548 - val_mse: 0.0548 - val_mae: 0.2136 - lr: 0.0010 - 265ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0650 - val_loss: 0.0632 - val_mse: 0.0632 - val_mae: 0.2304 - lr: 0.0010 - 240ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0685 - val_loss: 0.0450 - val_mse: 0.0450 - val_mae: 0.1907 - lr: 0.0010 - 227ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00013: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0092 - mse: 0.0092 - mae: 0.0769 - val_loss: 0.0858 - val_mse: 0.0858 - val_mae: 0.2735 - lr: 0.0010 - 210ms/epoch - 4ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0259 - mse: 0.0259 - mae: 0.1392 - val_loss: 0.0416 - val_mse: 0.0416 - val_mae: 0.1834 - lr: 1.0000e-04 - 237ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0117 - mse: 0.0117 - mae: 0.0881 - val_loss: 0.0401 - val_mse: 0.0401 - val_mae: 0.1790 - lr: 1.0000e-04 - 244ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.03656
48/48 - 0s - loss: 0.0103 - mse: 0.0103 - mae: 0.0820 - val_loss: 0.0374 - val_mse: 0.0374 - val_mae: 0.1717 - lr: 1.0000e-04 - 212ms/epoch - 4ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.03656 to 0.03439, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0733 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1635 - lr: 1.0000e-04 - 210ms/epoch - 4ms/step
Epoch 18/500

Epoch 00018: val_loss improved from 0.03439 to 0.03341, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0722 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1607 - lr: 1.0000e-04 - 251ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.03341 to 0.02962, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0663 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1495 - lr: 1.0000e-04 - 243ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss improved from 0.02962 to 0.02867, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0706 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1466 - lr: 1.0000e-04 - 271ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss improved from 0.02867 to 0.02677, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0657 - val_loss: 0.0268 - val_mse: 0.0268 - val_mae: 0.1406 - lr: 1.0000e-04 - 252ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.02677
48/48 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0631 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1410 - lr: 1.0000e-04 - 271ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02677
48/48 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0636 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1423 - lr: 1.0000e-04 - 232ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss improved from 0.02677 to 0.02567, saving model to LSTM7.h5
48/48 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0582 - val_loss: 0.0257 - val_mse: 0.0257 - val_mae: 0.1371 - lr: 1.0000e-04 - 242ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0643 - val_loss: 0.0259 - val_mse: 0.0259 - val_mae: 0.1380 - lr: 1.0000e-04 - 198ms/epoch - 4ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0625 - val_loss: 0.0275 - val_mse: 0.0275 - val_mae: 0.1432 - lr: 1.0000e-04 - 200ms/epoch - 4ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0583 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1469 - lr: 1.0000e-04 - 226ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0598 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1493 - lr: 1.0000e-04 - 207ms/epoch - 4ms/step
Epoch 29/500

Epoch 00029: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00029: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0618 - val_loss: 0.0302 - val_mse: 0.0302 - val_mae: 0.1514 - lr: 1.0000e-04 - 199ms/epoch - 4ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0567 - val_loss: 0.0305 - val_mse: 0.0305 - val_mae: 0.1522 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0551 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1532 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0545 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1545 - lr: 1.0000e-05 - 213ms/epoch - 4ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0557 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1551 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00034: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0542 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1556 - lr: 1.0000e-05 - 215ms/epoch - 4ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0555 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1561 - lr: 1.0000e-05 - 199ms/epoch - 4ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0564 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1567 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0556 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1573 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0544 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1575 - lr: 1.0000e-05 - 207ms/epoch - 4ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0539 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1571 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0543 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1567 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0555 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1568 - lr: 1.0000e-05 - 241ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0534 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1576 - lr: 1.0000e-05 - 205ms/epoch - 4ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0570 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1578 - lr: 1.0000e-05 - 207ms/epoch - 4ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0513 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1589 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0547 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1591 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0552 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1594 - lr: 1.0000e-05 - 201ms/epoch - 4ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0523 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1594 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0523 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1598 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0525 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1589 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0533 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1594 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0531 - val_loss: 0.0330 - val_mse: 0.0330 - val_mae: 0.1596 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0552 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1591 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0520 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1590 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0537 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1583 - lr: 1.0000e-05 - 241ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0517 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1583 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0542 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1589 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0515 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1591 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0536 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1589 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0546 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1592 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0551 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1581 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0540 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1583 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0557 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1585 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0515 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1593 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0540 - val_loss: 0.0328 - val_mse: 0.0328 - val_mae: 0.1590 - lr: 1.0000e-05 - 241ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0551 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1580 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0520 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1578 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0529 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1599 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0551 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1602 - lr: 1.0000e-05 - 202ms/epoch - 4ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0550 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1597 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0525 - val_loss: 0.0330 - val_mse: 0.0330 - val_mae: 0.1595 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1604 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0517 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1603 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0534 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1610 - lr: 1.0000e-05 - 203ms/epoch - 4ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.02567
48/48 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0553 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1607 - lr: 1.0000e-05 - 209ms/epoch - 4ms/step
Epoch 00074: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.80 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.48 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15952.568, Time=15.31 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=8.47 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16628.634, Time=10.28 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16462.206, Time=25.09 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16848.298, Time=13.32 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.023, Time=6.57 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.619, Time=3.49 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=7.51 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=18.51 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.994, Time=4.01 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.667, Time=4.43 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 126.280 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        18:17:53   BIC                         -16911.966
Sample:                             0   HQIC                        -17010.204
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -2.316e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x2         -2.309e-10   6.24e-05   -3.7e-06      1.000      -0.000       0.000
x3         -2.325e-10   6.26e-05  -3.71e-06      1.000      -0.000       0.000
x4             1.0000   6.25e-05    1.6e+04      0.000       1.000       1.000
x5         -2.107e-10   5.96e-05  -3.54e-06      1.000      -0.000       0.000
x6         -7.997e-10      0.000  -7.41e-06      1.000      -0.000       0.000
x7         -2.295e-10   6.22e-05  -3.69e-06      1.000      -0.000       0.000
x8         -2.246e-10   6.15e-05  -3.65e-06      1.000      -0.000       0.000
x9         -1.167e-11   1.25e-05  -9.33e-07      1.000   -2.45e-05    2.45e-05
x10        -4.454e-11   2.66e-05  -1.68e-06      1.000   -5.21e-05    5.21e-05
x11        -2.221e-10   6.11e-05  -3.63e-06      1.000      -0.000       0.000
x12        -2.266e-10   6.18e-05  -3.66e-06      1.000      -0.000       0.000
x13        -2.315e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x14        -1.767e-09      0.000  -1.02e-05      1.000      -0.000       0.000
x15         -2.11e-10   5.93e-05  -3.56e-06      1.000      -0.000       0.000
x16        -5.283e-10   9.45e-05  -5.59e-06      1.000      -0.000       0.000
x17        -2.098e-10   6.01e-05  -3.49e-06      1.000      -0.000       0.000
x18         -3.82e-11   2.41e-05  -1.58e-06      1.000   -4.73e-05    4.73e-05
x19        -2.645e-10   6.61e-05     -4e-06      1.000      -0.000       0.000
x20        -2.417e-10   6.21e-05  -3.89e-06      1.000      -0.000       0.000
x21        -4.824e-10   8.83e-05  -5.46e-06      1.000      -0.000       0.000
x22        -3.758e-13   1.19e-11     -0.032      0.975   -2.36e-11    2.29e-11
x23        -1.089e-11   8.42e-11     -0.129      0.897   -1.76e-10    1.54e-10
x24        -2.538e-09      0.000  -1.44e-05      1.000      -0.000       0.000
x25        -2.038e-09      0.000  -1.49e-05      1.000      -0.000       0.000
x26         -3.16e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x27        -2.955e-09      0.000  -1.32e-05      1.000      -0.000       0.000
x28        -1.664e-09      0.000  -9.94e-06      1.000      -0.000       0.000
x29        -1.568e-09      0.000  -9.63e-06      1.000      -0.000       0.000
ar.L1         -0.4923    6.2e-10  -7.94e+08      0.000      -0.492      -0.492
ar.L2         -0.1923    3.6e-10  -5.35e+08      0.000      -0.192      -0.192
ar.L3         -0.0462   1.71e-10  -2.71e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.41e-09  -5.04e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  51.79   Jarque-Bera (JB):           4012066.18
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.44
Prob(H) (two-sided):                  0.00   Kurtosis:                       348.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 5.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.07245, saving model to LSTM7.h5
16/16 - 2s - loss: 0.6031 - mse: 0.6031 - mae: 0.5816 - val_loss: 0.0725 - val_mse: 0.0725 - val_mae: 0.2175 - lr: 0.0010 - 2s/epoch - 131ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.07245 to 0.05426, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0982 - mse: 0.0982 - mae: 0.2715 - val_loss: 0.0543 - val_mse: 0.0543 - val_mae: 0.1852 - lr: 0.0010 - 96ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.05426 to 0.05329, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0308 - mse: 0.0308 - mae: 0.1410 - val_loss: 0.0533 - val_mse: 0.0533 - val_mae: 0.1773 - lr: 0.0010 - 95ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.05329 to 0.05020, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0288 - mse: 0.0288 - mae: 0.1320 - val_loss: 0.0502 - val_mse: 0.0502 - val_mae: 0.1733 - lr: 0.0010 - 86ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05020
16/16 - 0s - loss: 0.0203 - mse: 0.0203 - mae: 0.1148 - val_loss: 0.0509 - val_mse: 0.0509 - val_mae: 0.1750 - lr: 0.0010 - 81ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.05020 to 0.04909, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0208 - mse: 0.0208 - mae: 0.1149 - val_loss: 0.0491 - val_mse: 0.0491 - val_mae: 0.1715 - lr: 0.0010 - 89ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.04909 to 0.04433, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0177 - mse: 0.0177 - mae: 0.1061 - val_loss: 0.0443 - val_mse: 0.0443 - val_mae: 0.1622 - lr: 0.0010 - 88ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.04433 to 0.04346, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0156 - mse: 0.0156 - mae: 0.0996 - val_loss: 0.0435 - val_mse: 0.0435 - val_mae: 0.1608 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.04346 to 0.04201, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0146 - mse: 0.0146 - mae: 0.0956 - val_loss: 0.0420 - val_mse: 0.0420 - val_mae: 0.1584 - lr: 0.0010 - 107ms/epoch - 7ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.04201 to 0.04017, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0151 - mse: 0.0151 - mae: 0.0974 - val_loss: 0.0402 - val_mse: 0.0402 - val_mae: 0.1550 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.04017
16/16 - 0s - loss: 0.0124 - mse: 0.0124 - mae: 0.0884 - val_loss: 0.0406 - val_mse: 0.0406 - val_mae: 0.1565 - lr: 0.0010 - 97ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.04017 to 0.03925, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0125 - mse: 0.0125 - mae: 0.0892 - val_loss: 0.0393 - val_mse: 0.0393 - val_mae: 0.1539 - lr: 0.0010 - 95ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.03925 to 0.03614, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0116 - mse: 0.0116 - mae: 0.0868 - val_loss: 0.0361 - val_mse: 0.0361 - val_mae: 0.1468 - lr: 0.0010 - 99ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.03614 to 0.03480, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0120 - mse: 0.0120 - mae: 0.0858 - val_loss: 0.0348 - val_mse: 0.0348 - val_mae: 0.1438 - lr: 0.0010 - 89ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss improved from 0.03480 to 0.03399, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0094 - mse: 0.0094 - mae: 0.0779 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1421 - lr: 0.0010 - 108ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0109 - mse: 0.0109 - mae: 0.0816 - val_loss: 0.0355 - val_mse: 0.0355 - val_mae: 0.1467 - lr: 0.0010 - 74ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0100 - mse: 0.0100 - mae: 0.0800 - val_loss: 0.0348 - val_mse: 0.0348 - val_mae: 0.1455 - lr: 0.0010 - 78ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0093 - mse: 0.0093 - mae: 0.0776 - val_loss: 0.0355 - val_mse: 0.0355 - val_mae: 0.1480 - lr: 0.0010 - 88ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0094 - mse: 0.0094 - mae: 0.0751 - val_loss: 0.0364 - val_mse: 0.0364 - val_mae: 0.1509 - lr: 0.0010 - 85ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00020: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0084 - mse: 0.0084 - mae: 0.0730 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1479 - lr: 0.0010 - 96ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0715 - val_loss: 0.0347 - val_mse: 0.0347 - val_mae: 0.1473 - lr: 1.0000e-04 - 95ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0712 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1481 - lr: 1.0000e-04 - 83ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0705 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1481 - lr: 1.0000e-04 - 77ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0673 - val_loss: 0.0348 - val_mse: 0.0348 - val_mae: 0.1477 - lr: 1.0000e-04 - 76ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00025: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0728 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1467 - lr: 1.0000e-04 - 80ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0733 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1467 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0679 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1467 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0086 - mse: 0.0086 - mae: 0.0743 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1466 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0715 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1466 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00030: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0717 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1466 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0711 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0088 - mse: 0.0088 - mae: 0.0734 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0091 - mse: 0.0091 - mae: 0.0733 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0703 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1466 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0695 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1466 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0692 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0713 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0693 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1464 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0717 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1464 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0697 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1464 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0725 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0688 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1464 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0689 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1464 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0708 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1464 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0690 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0726 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1464 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0090 - mse: 0.0090 - mae: 0.0748 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0669 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1464 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0684 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0692 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0730 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0683 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0689 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1461 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0684 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1461 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0714 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1462 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0692 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1463 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0702 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1465 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0700 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1465 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0712 - val_loss: 0.0343 - val_mse: 0.0343 - val_mae: 0.1466 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0686 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1465 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0086 - mse: 0.0086 - mae: 0.0717 - val_loss: 0.0342 - val_mse: 0.0342 - val_mae: 0.1464 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0704 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1463 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0654 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1462 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.03399
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0709 - val_loss: 0.0341 - val_mse: 0.0341 - val_mae: 0.1461 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss improved from 0.03399 to 0.03398, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0715 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1458 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss improved from 0.03398 to 0.03393, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0707 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1457 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.03393
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0694 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1458 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.03393
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0687 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1459 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.03393
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0666 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1459 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.03393
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0689 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1458 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.03393
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0702 - val_loss: 0.0340 - val_mse: 0.0340 - val_mae: 0.1458 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 72/500

Epoch 00072: val_loss improved from 0.03393 to 0.03390, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0700 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1457 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 73/500

Epoch 00073: val_loss improved from 0.03390 to 0.03384, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0689 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1455 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.03384
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0690 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1456 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.03384
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0687 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1457 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.03384
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0704 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1456 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 77/500

Epoch 00077: val_loss improved from 0.03384 to 0.03380, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0693 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1455 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0667 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1456 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0088 - mse: 0.0088 - mae: 0.0735 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1456 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0676 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1456 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0726 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1457 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0085 - mse: 0.0085 - mae: 0.0714 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1457 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0693 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1459 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 84/500

Epoch 00084: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0706 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1458 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 85/500

Epoch 00085: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0678 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1458 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 86/500

Epoch 00086: val_loss did not improve from 0.03380
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0691 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1457 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 87/500

Epoch 00087: val_loss improved from 0.03380 to 0.03380, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0698 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1456 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 88/500

Epoch 00088: val_loss improved from 0.03380 to 0.03380, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0693 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1456 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 89/500

Epoch 00089: val_loss improved from 0.03380 to 0.03375, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0686 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1455 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 90/500

Epoch 00090: val_loss improved from 0.03375 to 0.03368, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0677 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1453 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 91/500

Epoch 00091: val_loss improved from 0.03368 to 0.03367, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0719 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1453 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 92/500

Epoch 00092: val_loss improved from 0.03367 to 0.03367, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0683 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1453 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 93/500

Epoch 00093: val_loss did not improve from 0.03367
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0666 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1455 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 94/500

Epoch 00094: val_loss did not improve from 0.03367
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0695 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1454 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 95/500

Epoch 00095: val_loss improved from 0.03367 to 0.03358, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0703 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1451 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 96/500

Epoch 00096: val_loss improved from 0.03358 to 0.03357, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0645 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1451 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 97/500

Epoch 00097: val_loss improved from 0.03357 to 0.03356, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0670 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1451 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 98/500

Epoch 00098: val_loss did not improve from 0.03356
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0661 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1451 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 99/500

Epoch 00099: val_loss improved from 0.03356 to 0.03352, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0677 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1450 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 100/500

Epoch 00100: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0665 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1453 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 101/500

Epoch 00101: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0679 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1454 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 102/500

Epoch 00102: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0686 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1456 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 103/500

Epoch 00103: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0687 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1456 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 104/500

Epoch 00104: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0703 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1457 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 105/500

Epoch 00105: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0686 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1457 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 106/500

Epoch 00106: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0687 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1456 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 107/500

Epoch 00107: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0679 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1455 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 108/500

Epoch 00108: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0664 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1455 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 109/500

Epoch 00109: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0697 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1457 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 110/500

Epoch 00110: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0701 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1461 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 111/500

Epoch 00111: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0683 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1462 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 112/500

Epoch 00112: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0716 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1462 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 113/500

Epoch 00113: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0667 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1462 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 114/500

Epoch 00114: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0695 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1459 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 115/500

Epoch 00115: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0660 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1460 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 116/500

Epoch 00116: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0706 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1460 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 117/500

Epoch 00117: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0684 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1462 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 118/500

Epoch 00118: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0652 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1463 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 119/500

Epoch 00119: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0680 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1463 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 120/500

Epoch 00120: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0681 - val_loss: 0.0338 - val_mse: 0.0338 - val_mae: 0.1462 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 121/500

Epoch 00121: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0683 - val_loss: 0.0339 - val_mse: 0.0339 - val_mae: 0.1463 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 122/500

Epoch 00122: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0697 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1460 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 123/500

Epoch 00123: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0671 - val_loss: 0.0337 - val_mse: 0.0337 - val_mae: 0.1460 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 124/500

Epoch 00124: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0684 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1458 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 125/500

Epoch 00125: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0678 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1456 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 126/500

Epoch 00126: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0084 - mse: 0.0084 - mae: 0.0726 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1457 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 127/500

Epoch 00127: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0678 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1456 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 128/500

Epoch 00128: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0688 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1456 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 129/500

Epoch 00129: val_loss did not improve from 0.03352
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0662 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1456 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 130/500

Epoch 00130: val_loss improved from 0.03352 to 0.03350, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0708 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1455 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 131/500

Epoch 00131: val_loss did not improve from 0.03350
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0639 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1455 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 132/500

Epoch 00132: val_loss improved from 0.03350 to 0.03347, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0718 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1454 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 133/500

Epoch 00133: val_loss did not improve from 0.03347
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0703 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1457 - lr: 1.0000e-05 - 74ms/epoch - 5ms/step
Epoch 134/500

Epoch 00134: val_loss did not improve from 0.03347
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0661 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1456 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 135/500

Epoch 00135: val_loss improved from 0.03347 to 0.03346, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0656 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1455 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 136/500

Epoch 00136: val_loss improved from 0.03346 to 0.03336, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0683 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1452 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 137/500

Epoch 00137: val_loss improved from 0.03336 to 0.03328, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0695 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1450 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 138/500

Epoch 00138: val_loss improved from 0.03328 to 0.03321, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0087 - mse: 0.0087 - mae: 0.0741 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1449 - lr: 1.0000e-05 - 118ms/epoch - 7ms/step
Epoch 139/500

Epoch 00139: val_loss improved from 0.03321 to 0.03318, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0634 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1448 - lr: 1.0000e-05 - 111ms/epoch - 7ms/step
Epoch 140/500

Epoch 00140: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0656 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1450 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 141/500

Epoch 00141: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0631 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1451 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 142/500

Epoch 00142: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0663 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1454 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 143/500

Epoch 00143: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0672 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1455 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 144/500

Epoch 00144: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0709 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1456 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 145/500

Epoch 00145: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0682 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1456 - lr: 1.0000e-05 - 74ms/epoch - 5ms/step
Epoch 146/500

Epoch 00146: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0680 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1458 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 147/500

Epoch 00147: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0637 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1460 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 148/500

Epoch 00148: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0646 - val_loss: 0.0336 - val_mse: 0.0336 - val_mae: 0.1461 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 149/500

Epoch 00149: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0663 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1459 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 150/500

Epoch 00150: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0662 - val_loss: 0.0335 - val_mse: 0.0335 - val_mae: 0.1458 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 151/500

Epoch 00151: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0651 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1455 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 152/500

Epoch 00152: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0653 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1455 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 153/500

Epoch 00153: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0688 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1454 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 154/500

Epoch 00154: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0669 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1456 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 155/500

Epoch 00155: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0637 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1458 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 156/500

Epoch 00156: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0692 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1458 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 157/500

Epoch 00157: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0701 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1455 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 158/500

Epoch 00158: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0655 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1453 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 159/500

Epoch 00159: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0665 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1455 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 160/500

Epoch 00160: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0652 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1458 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 161/500

Epoch 00161: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0685 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1456 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 162/500

Epoch 00162: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0683 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1456 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 163/500

Epoch 00163: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0663 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1458 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 164/500

Epoch 00164: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0674 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1458 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 165/500

Epoch 00165: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0679 - val_loss: 0.0334 - val_mse: 0.0334 - val_mae: 0.1459 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 166/500

Epoch 00166: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0678 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1455 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 167/500

Epoch 00167: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0665 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1457 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 168/500

Epoch 00168: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0673 - val_loss: 0.0333 - val_mse: 0.0333 - val_mae: 0.1457 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 169/500

Epoch 00169: val_loss did not improve from 0.03318
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0655 - val_loss: 0.0332 - val_mse: 0.0332 - val_mae: 0.1456 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 170/500

Epoch 00170: val_loss improved from 0.03318 to 0.03314, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0683 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1454 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 171/500

Epoch 00171: val_loss improved from 0.03314 to 0.03308, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0646 - val_loss: 0.0331 - val_mse: 0.0331 - val_mae: 0.1452 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 172/500

Epoch 00172: val_loss improved from 0.03308 to 0.03294, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0662 - val_loss: 0.0329 - val_mse: 0.0329 - val_mae: 0.1448 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 173/500

Epoch 00173: val_loss improved from 0.03294 to 0.03271, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0669 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1442 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 174/500

Epoch 00174: val_loss improved from 0.03271 to 0.03261, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0659 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1440 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 175/500

Epoch 00175: val_loss did not improve from 0.03261
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0641 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1442 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 176/500

Epoch 00176: val_loss did not improve from 0.03261
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0653 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1443 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 177/500

Epoch 00177: val_loss did not improve from 0.03261
16/16 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0685 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1442 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 178/500

Epoch 00178: val_loss did not improve from 0.03261
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0639 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1443 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 179/500

Epoch 00179: val_loss did not improve from 0.03261
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0622 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1442 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 180/500

Epoch 00180: val_loss improved from 0.03261 to 0.03253, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0639 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1439 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 181/500

Epoch 00181: val_loss improved from 0.03253 to 0.03239, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0658 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1435 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 182/500

Epoch 00182: val_loss improved from 0.03239 to 0.03237, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0661 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1435 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 183/500

Epoch 00183: val_loss improved from 0.03237 to 0.03219, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0667 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1430 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 184/500

Epoch 00184: val_loss improved from 0.03219 to 0.03213, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0641 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1429 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 185/500

Epoch 00185: val_loss improved from 0.03213 to 0.03205, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0654 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1427 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 186/500

Epoch 00186: val_loss improved from 0.03205 to 0.03193, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0667 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1423 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 187/500

Epoch 00187: val_loss improved from 0.03193 to 0.03192, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0659 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1423 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 188/500

Epoch 00188: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0671 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1425 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 189/500

Epoch 00189: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0666 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1428 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 190/500

Epoch 00190: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0631 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1430 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 191/500

Epoch 00191: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0673 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1432 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 192/500

Epoch 00192: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0640 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1436 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 193/500

Epoch 00193: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0659 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1438 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 194/500

Epoch 00194: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0672 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1438 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 195/500

Epoch 00195: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0664 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1439 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 196/500

Epoch 00196: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0656 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1438 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 197/500

Epoch 00197: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0618 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1435 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 198/500

Epoch 00198: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0653 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1435 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 199/500

Epoch 00199: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0623 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1438 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 200/500

Epoch 00200: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0656 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1439 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 201/500

Epoch 00201: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0653 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1441 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 202/500

Epoch 00202: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0657 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1441 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 203/500

Epoch 00203: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0639 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1440 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 204/500

Epoch 00204: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0648 - val_loss: 0.0325 - val_mse: 0.0325 - val_mae: 0.1443 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 205/500

Epoch 00205: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0652 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1447 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 206/500

Epoch 00206: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0632 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1448 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 207/500

Epoch 00207: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0646 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1449 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 208/500

Epoch 00208: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0638 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1452 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 209/500

Epoch 00209: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0660 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1452 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 210/500

Epoch 00210: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0688 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1449 - lr: 1.0000e-05 - 74ms/epoch - 5ms/step
Epoch 211/500

Epoch 00211: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0641 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1448 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 212/500

Epoch 00212: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0651 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1453 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 213/500

Epoch 00213: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0628 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1453 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 214/500

Epoch 00214: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0634 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1452 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 215/500

Epoch 00215: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0624 - val_loss: 0.0327 - val_mse: 0.0327 - val_mae: 0.1453 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 216/500

Epoch 00216: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0644 - val_loss: 0.0326 - val_mse: 0.0326 - val_mae: 0.1449 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 217/500

Epoch 00217: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0639 - val_loss: 0.0324 - val_mse: 0.0324 - val_mae: 0.1443 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 218/500

Epoch 00218: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0621 - val_loss: 0.0323 - val_mse: 0.0323 - val_mae: 0.1443 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 219/500

Epoch 00219: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0633 - val_loss: 0.0322 - val_mse: 0.0322 - val_mae: 0.1439 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 220/500

Epoch 00220: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0634 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1434 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 221/500

Epoch 00221: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0639 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1434 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 222/500

Epoch 00222: val_loss did not improve from 0.03192
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0634 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1434 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 223/500

Epoch 00223: val_loss improved from 0.03192 to 0.03191, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0625 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1433 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 224/500

Epoch 00224: val_loss improved from 0.03191 to 0.03190, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0650 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1433 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 225/500

Epoch 00225: val_loss did not improve from 0.03190
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0619 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1434 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 226/500

Epoch 00226: val_loss did not improve from 0.03190
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0630 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1434 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 227/500

Epoch 00227: val_loss improved from 0.03190 to 0.03186, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0626 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1432 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 228/500

Epoch 00228: val_loss improved from 0.03186 to 0.03178, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0650 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1430 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 229/500

Epoch 00229: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0630 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1433 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 230/500

Epoch 00230: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0602 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1439 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 231/500

Epoch 00231: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0643 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1439 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 232/500

Epoch 00232: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0621 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1436 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 233/500

Epoch 00233: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0582 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1437 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 234/500

Epoch 00234: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0642 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1436 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 235/500

Epoch 00235: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0650 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1437 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 236/500

Epoch 00236: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0652 - val_loss: 0.0321 - val_mse: 0.0321 - val_mae: 0.1441 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 237/500

Epoch 00237: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0615 - val_loss: 0.0320 - val_mse: 0.0320 - val_mae: 0.1440 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 238/500

Epoch 00238: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0654 - val_loss: 0.0319 - val_mse: 0.0319 - val_mae: 0.1435 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 239/500

Epoch 00239: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0606 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1435 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 240/500

Epoch 00240: val_loss did not improve from 0.03178
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0647 - val_loss: 0.0318 - val_mse: 0.0318 - val_mae: 0.1434 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 241/500

Epoch 00241: val_loss improved from 0.03178 to 0.03168, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0619 - val_loss: 0.0317 - val_mse: 0.0317 - val_mae: 0.1431 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 242/500

Epoch 00242: val_loss improved from 0.03168 to 0.03161, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0627 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1429 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 243/500

Epoch 00243: val_loss did not improve from 0.03161
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0621 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1430 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 244/500

Epoch 00244: val_loss improved from 0.03161 to 0.03161, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0634 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1430 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 245/500

Epoch 00245: val_loss improved from 0.03161 to 0.03150, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0661 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1428 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 246/500

Epoch 00246: val_loss improved from 0.03150 to 0.03136, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0631 - val_loss: 0.0314 - val_mse: 0.0314 - val_mae: 0.1424 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 247/500

Epoch 00247: val_loss improved from 0.03136 to 0.03122, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0643 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1420 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 248/500

Epoch 00248: val_loss did not improve from 0.03122
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0625 - val_loss: 0.0314 - val_mse: 0.0314 - val_mae: 0.1425 - lr: 1.0000e-05 - 125ms/epoch - 8ms/step
Epoch 249/500

Epoch 00249: val_loss did not improve from 0.03122
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0638 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1429 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 250/500

Epoch 00250: val_loss did not improve from 0.03122
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0624 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1429 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 251/500

Epoch 00251: val_loss did not improve from 0.03122
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0638 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1428 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 252/500

Epoch 00252: val_loss did not improve from 0.03122
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0627 - val_loss: 0.0313 - val_mse: 0.0313 - val_mae: 0.1423 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 253/500

Epoch 00253: val_loss improved from 0.03122 to 0.03121, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0659 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1421 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 254/500

Epoch 00254: val_loss did not improve from 0.03121
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0615 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1421 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 255/500

Epoch 00255: val_loss improved from 0.03121 to 0.03116, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0617 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1420 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 256/500

Epoch 00256: val_loss improved from 0.03116 to 0.03108, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0614 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1417 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 257/500

Epoch 00257: val_loss improved from 0.03108 to 0.03100, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0594 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1416 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 258/500

Epoch 00258: val_loss improved from 0.03100 to 0.03095, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0613 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1415 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 259/500

Epoch 00259: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0638 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1417 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 260/500

Epoch 00260: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0627 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1416 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 261/500

Epoch 00261: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0669 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1417 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 262/500

Epoch 00262: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0622 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1423 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 263/500

Epoch 00263: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0614 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1431 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 264/500

Epoch 00264: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0610 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1433 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 265/500

Epoch 00265: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0608 - val_loss: 0.0316 - val_mse: 0.0316 - val_mae: 0.1433 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 266/500

Epoch 00266: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0619 - val_loss: 0.0315 - val_mse: 0.0315 - val_mae: 0.1432 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 267/500

Epoch 00267: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0601 - val_loss: 0.0314 - val_mse: 0.0314 - val_mae: 0.1429 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 268/500

Epoch 00268: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0600 - val_loss: 0.0312 - val_mse: 0.0312 - val_mae: 0.1424 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 269/500

Epoch 00269: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0613 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1423 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 270/500

Epoch 00270: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0561 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1423 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 271/500

Epoch 00271: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0643 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1420 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 272/500

Epoch 00272: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0616 - val_loss: 0.0310 - val_mse: 0.0310 - val_mae: 0.1421 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 273/500

Epoch 00273: val_loss did not improve from 0.03095
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0593 - val_loss: 0.0311 - val_mse: 0.0311 - val_mae: 0.1423 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 274/500

Epoch 00274: val_loss improved from 0.03095 to 0.03077, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0603 - val_loss: 0.0308 - val_mse: 0.0308 - val_mae: 0.1414 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 275/500

Epoch 00275: val_loss improved from 0.03077 to 0.03056, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0612 - val_loss: 0.0306 - val_mse: 0.0306 - val_mae: 0.1408 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 276/500

Epoch 00276: val_loss improved from 0.03056 to 0.03049, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0613 - val_loss: 0.0305 - val_mse: 0.0305 - val_mae: 0.1406 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 277/500

Epoch 00277: val_loss did not improve from 0.03049
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0616 - val_loss: 0.0305 - val_mse: 0.0305 - val_mae: 0.1407 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 278/500

Epoch 00278: val_loss improved from 0.03049 to 0.03033, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0624 - val_loss: 0.0303 - val_mse: 0.0303 - val_mae: 0.1402 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 279/500

Epoch 00279: val_loss improved from 0.03033 to 0.03008, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0620 - val_loss: 0.0301 - val_mse: 0.0301 - val_mae: 0.1396 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 280/500

Epoch 00280: val_loss improved from 0.03008 to 0.03007, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0610 - val_loss: 0.0301 - val_mse: 0.0301 - val_mae: 0.1396 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 281/500

Epoch 00281: val_loss did not improve from 0.03007
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0605 - val_loss: 0.0302 - val_mse: 0.0302 - val_mae: 0.1399 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 282/500

Epoch 00282: val_loss did not improve from 0.03007
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0647 - val_loss: 0.0302 - val_mse: 0.0302 - val_mae: 0.1399 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 283/500

Epoch 00283: val_loss improved from 0.03007 to 0.02989, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0619 - val_loss: 0.0299 - val_mse: 0.0299 - val_mae: 0.1392 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 284/500

Epoch 00284: val_loss improved from 0.02989 to 0.02979, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0612 - val_loss: 0.0298 - val_mse: 0.0298 - val_mae: 0.1389 - lr: 1.0000e-05 - 130ms/epoch - 8ms/step
Epoch 285/500

Epoch 00285: val_loss improved from 0.02979 to 0.02966, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0600 - val_loss: 0.0297 - val_mse: 0.0297 - val_mae: 0.1386 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 286/500

Epoch 00286: val_loss improved from 0.02966 to 0.02951, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0595 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1382 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 287/500

Epoch 00287: val_loss improved from 0.02951 to 0.02924, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0625 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1374 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 288/500

Epoch 00288: val_loss improved from 0.02924 to 0.02917, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0626 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1372 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 289/500

Epoch 00289: val_loss improved from 0.02917 to 0.02916, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0599 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1372 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 290/500

Epoch 00290: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0608 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1373 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 291/500

Epoch 00291: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0585 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1373 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 292/500

Epoch 00292: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0593 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1373 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 293/500

Epoch 00293: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0590 - val_loss: 0.0294 - val_mse: 0.0294 - val_mae: 0.1380 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 294/500

Epoch 00294: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0608 - val_loss: 0.0294 - val_mse: 0.0294 - val_mae: 0.1381 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 295/500

Epoch 00295: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0584 - val_loss: 0.0294 - val_mse: 0.0294 - val_mae: 0.1382 - lr: 1.0000e-05 - 77ms/epoch - 5ms/step
Epoch 296/500

Epoch 00296: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0605 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1386 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 297/500

Epoch 00297: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0609 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1383 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 298/500

Epoch 00298: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0590 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1386 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 299/500

Epoch 00299: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0603 - val_loss: 0.0297 - val_mse: 0.0297 - val_mae: 0.1390 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 300/500

Epoch 00300: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0637 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1388 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 301/500

Epoch 00301: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0591 - val_loss: 0.0293 - val_mse: 0.0293 - val_mae: 0.1382 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 302/500

Epoch 00302: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0611 - val_loss: 0.0294 - val_mse: 0.0294 - val_mae: 0.1384 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 303/500

Epoch 00303: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0613 - val_loss: 0.0296 - val_mse: 0.0296 - val_mae: 0.1388 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 304/500

Epoch 00304: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0613 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1386 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 305/500

Epoch 00305: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0619 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1388 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 306/500

Epoch 00306: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0601 - val_loss: 0.0295 - val_mse: 0.0295 - val_mae: 0.1388 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 307/500

Epoch 00307: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0600 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1380 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 308/500

Epoch 00308: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0599 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1379 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 309/500

Epoch 00309: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0599 - val_loss: 0.0292 - val_mse: 0.0292 - val_mae: 0.1378 - lr: 1.0000e-05 - 73ms/epoch - 5ms/step
Epoch 310/500

Epoch 00310: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0589 - val_loss: 0.0293 - val_mse: 0.0293 - val_mae: 0.1381 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 311/500

Epoch 00311: val_loss did not improve from 0.02916
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0602 - val_loss: 0.0293 - val_mse: 0.0293 - val_mae: 0.1381 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 312/500

Epoch 00312: val_loss improved from 0.02916 to 0.02910, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0587 - val_loss: 0.0291 - val_mse: 0.0291 - val_mae: 0.1375 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 313/500

Epoch 00313: val_loss improved from 0.02910 to 0.02891, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0607 - val_loss: 0.0289 - val_mse: 0.0289 - val_mae: 0.1370 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 314/500

Epoch 00314: val_loss improved from 0.02891 to 0.02882, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0593 - val_loss: 0.0288 - val_mse: 0.0288 - val_mae: 0.1368 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 315/500

Epoch 00315: val_loss improved from 0.02882 to 0.02868, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0642 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1364 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 316/500

Epoch 00316: val_loss improved from 0.02868 to 0.02857, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0588 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1361 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 317/500

Epoch 00317: val_loss did not improve from 0.02857
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0602 - val_loss: 0.0287 - val_mse: 0.0287 - val_mae: 0.1364 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 318/500

Epoch 00318: val_loss did not improve from 0.02857
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0602 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1362 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 319/500

Epoch 00319: val_loss did not improve from 0.02857
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0586 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1363 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 320/500

Epoch 00320: val_loss did not improve from 0.02857
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0608 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1363 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 321/500

Epoch 00321: val_loss improved from 0.02857 to 0.02848, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0619 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1361 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 322/500

Epoch 00322: val_loss did not improve from 0.02848
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0580 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1363 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 323/500

Epoch 00323: val_loss did not improve from 0.02848
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0595 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1364 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 324/500

Epoch 00324: val_loss did not improve from 0.02848
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0570 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1363 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 325/500

Epoch 00325: val_loss improved from 0.02848 to 0.02838, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0605 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1358 - lr: 1.0000e-05 - 121ms/epoch - 8ms/step
Epoch 326/500

Epoch 00326: val_loss improved from 0.02838 to 0.02836, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0586 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1358 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 327/500

Epoch 00327: val_loss did not improve from 0.02836
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0581 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1362 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 328/500

Epoch 00328: val_loss did not improve from 0.02836
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0570 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1362 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 329/500

Epoch 00329: val_loss improved from 0.02836 to 0.02826, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0597 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1356 - lr: 1.0000e-05 - 107ms/epoch - 7ms/step
Epoch 330/500

Epoch 00330: val_loss did not improve from 0.02826
16/16 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0611 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1358 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 331/500

Epoch 00331: val_loss did not improve from 0.02826
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0603 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1364 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 332/500

Epoch 00332: val_loss did not improve from 0.02826
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0597 - val_loss: 0.0286 - val_mse: 0.0286 - val_mae: 0.1367 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 333/500

Epoch 00333: val_loss did not improve from 0.02826
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0599 - val_loss: 0.0285 - val_mse: 0.0285 - val_mae: 0.1364 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 334/500

Epoch 00334: val_loss did not improve from 0.02826
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0578 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1360 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 335/500

Epoch 00335: val_loss did not improve from 0.02826
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0573 - val_loss: 0.0283 - val_mse: 0.0283 - val_mae: 0.1358 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 336/500

Epoch 00336: val_loss improved from 0.02826 to 0.02824, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0569 - val_loss: 0.0282 - val_mse: 0.0282 - val_mae: 0.1358 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 337/500

Epoch 00337: val_loss improved from 0.02824 to 0.02795, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0595 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1350 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 338/500

Epoch 00338: val_loss improved from 0.02795 to 0.02794, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0591 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1350 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 339/500

Epoch 00339: val_loss did not improve from 0.02794
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0576 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1352 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 340/500

Epoch 00340: val_loss did not improve from 0.02794
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0578 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1356 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 341/500

Epoch 00341: val_loss did not improve from 0.02794
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0570 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1355 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 342/500

Epoch 00342: val_loss did not improve from 0.02794
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0583 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1355 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 343/500

Epoch 00343: val_loss did not improve from 0.02794
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0579 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1356 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 344/500

Epoch 00344: val_loss did not improve from 0.02794
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0598 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1352 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 345/500

Epoch 00345: val_loss improved from 0.02794 to 0.02793, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0568 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1351 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 346/500

Epoch 00346: val_loss did not improve from 0.02793
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0594 - val_loss: 0.0280 - val_mse: 0.0280 - val_mae: 0.1353 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 347/500

Epoch 00347: val_loss did not improve from 0.02793
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0568 - val_loss: 0.0281 - val_mse: 0.0281 - val_mae: 0.1356 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 348/500

Epoch 00348: val_loss improved from 0.02793 to 0.02792, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0597 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1352 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 349/500

Epoch 00349: val_loss improved from 0.02792 to 0.02786, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0577 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1350 - lr: 1.0000e-05 - 118ms/epoch - 7ms/step
Epoch 350/500

Epoch 00350: val_loss improved from 0.02786 to 0.02780, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0567 - val_loss: 0.0278 - val_mse: 0.0278 - val_mae: 0.1348 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 351/500

Epoch 00351: val_loss improved from 0.02780 to 0.02757, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0554 - val_loss: 0.0276 - val_mse: 0.0276 - val_mae: 0.1341 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 352/500

Epoch 00352: val_loss improved from 0.02757 to 0.02746, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0595 - val_loss: 0.0275 - val_mse: 0.0275 - val_mae: 0.1338 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 353/500

Epoch 00353: val_loss improved from 0.02746 to 0.02734, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0575 - val_loss: 0.0273 - val_mse: 0.0273 - val_mae: 0.1335 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 354/500

Epoch 00354: val_loss improved from 0.02734 to 0.02710, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0575 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1328 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 355/500

Epoch 00355: val_loss improved from 0.02710 to 0.02709, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0576 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1329 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 356/500

Epoch 00356: val_loss did not improve from 0.02709
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0602 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1330 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 357/500

Epoch 00357: val_loss did not improve from 0.02709
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0589 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1333 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 358/500

Epoch 00358: val_loss did not improve from 0.02709
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0576 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1334 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 359/500

Epoch 00359: val_loss did not improve from 0.02709
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0601 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1332 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 360/500

Epoch 00360: val_loss improved from 0.02709 to 0.02703, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0599 - val_loss: 0.0270 - val_mse: 0.0270 - val_mae: 0.1328 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 361/500

Epoch 00361: val_loss improved from 0.02703 to 0.02695, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0573 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1326 - lr: 1.0000e-05 - 115ms/epoch - 7ms/step
Epoch 362/500

Epoch 00362: val_loss improved from 0.02695 to 0.02691, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0564 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1325 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 363/500

Epoch 00363: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0579 - val_loss: 0.0270 - val_mse: 0.0270 - val_mae: 0.1327 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 364/500

Epoch 00364: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0582 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1330 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 365/500

Epoch 00365: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0597 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1334 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 366/500

Epoch 00366: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0272 - val_mse: 0.0272 - val_mae: 0.1336 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 367/500

Epoch 00367: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0572 - val_loss: 0.0271 - val_mse: 0.0271 - val_mae: 0.1333 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 368/500

Epoch 00368: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0591 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1327 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 369/500

Epoch 00369: val_loss did not improve from 0.02691
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0586 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1327 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 370/500

Epoch 00370: val_loss improved from 0.02691 to 0.02685, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0582 - val_loss: 0.0269 - val_mse: 0.0269 - val_mae: 0.1325 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 371/500

Epoch 00371: val_loss improved from 0.02685 to 0.02660, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0568 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1317 - lr: 1.0000e-05 - 121ms/epoch - 8ms/step
Epoch 372/500

Epoch 00372: val_loss improved from 0.02660 to 0.02651, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0605 - val_loss: 0.0265 - val_mse: 0.0265 - val_mae: 0.1315 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 373/500

Epoch 00373: val_loss improved from 0.02651 to 0.02637, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0571 - val_loss: 0.0264 - val_mse: 0.0264 - val_mae: 0.1311 - lr: 1.0000e-05 - 110ms/epoch - 7ms/step
Epoch 374/500

Epoch 00374: val_loss did not improve from 0.02637
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0571 - val_loss: 0.0264 - val_mse: 0.0264 - val_mae: 0.1312 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 375/500

Epoch 00375: val_loss improved from 0.02637 to 0.02636, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0588 - val_loss: 0.0264 - val_mse: 0.0264 - val_mae: 0.1311 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 376/500

Epoch 00376: val_loss improved from 0.02636 to 0.02627, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0578 - val_loss: 0.0263 - val_mse: 0.0263 - val_mae: 0.1309 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 377/500

Epoch 00377: val_loss did not improve from 0.02627
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0551 - val_loss: 0.0263 - val_mse: 0.0263 - val_mae: 0.1311 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 378/500

Epoch 00378: val_loss improved from 0.02627 to 0.02616, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0584 - val_loss: 0.0262 - val_mse: 0.0262 - val_mae: 0.1306 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 379/500

Epoch 00379: val_loss did not improve from 0.02616
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0552 - val_loss: 0.0262 - val_mse: 0.0262 - val_mae: 0.1307 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 380/500

Epoch 00380: val_loss improved from 0.02616 to 0.02615, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0556 - val_loss: 0.0262 - val_mse: 0.0262 - val_mae: 0.1306 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 381/500

Epoch 00381: val_loss improved from 0.02615 to 0.02593, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0568 - val_loss: 0.0259 - val_mse: 0.0259 - val_mae: 0.1299 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 382/500

Epoch 00382: val_loss did not improve from 0.02593
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0587 - val_loss: 0.0260 - val_mse: 0.0260 - val_mae: 0.1300 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 383/500

Epoch 00383: val_loss did not improve from 0.02593
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0586 - val_loss: 0.0260 - val_mse: 0.0260 - val_mae: 0.1301 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 384/500

Epoch 00384: val_loss improved from 0.02593 to 0.02583, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0597 - val_loss: 0.0258 - val_mse: 0.0258 - val_mae: 0.1297 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 385/500

Epoch 00385: val_loss improved from 0.02583 to 0.02577, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0562 - val_loss: 0.0258 - val_mse: 0.0258 - val_mae: 0.1295 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 386/500

Epoch 00386: val_loss improved from 0.02577 to 0.02556, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0580 - val_loss: 0.0256 - val_mse: 0.0256 - val_mae: 0.1288 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 387/500

Epoch 00387: val_loss improved from 0.02556 to 0.02552, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0565 - val_loss: 0.0255 - val_mse: 0.0255 - val_mae: 0.1288 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 388/500

Epoch 00388: val_loss improved from 0.02552 to 0.02527, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0547 - val_loss: 0.0253 - val_mse: 0.0253 - val_mae: 0.1280 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 389/500

Epoch 00389: val_loss improved from 0.02527 to 0.02519, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0559 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1278 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 390/500

Epoch 00390: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0563 - val_loss: 0.0253 - val_mse: 0.0253 - val_mae: 0.1281 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 391/500

Epoch 00391: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0566 - val_loss: 0.0255 - val_mse: 0.0255 - val_mae: 0.1287 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 392/500

Epoch 00392: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0582 - val_loss: 0.0256 - val_mse: 0.0256 - val_mae: 0.1293 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 393/500

Epoch 00393: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0568 - val_loss: 0.0256 - val_mse: 0.0256 - val_mae: 0.1293 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 394/500

Epoch 00394: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0586 - val_loss: 0.0256 - val_mse: 0.0256 - val_mae: 0.1291 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 395/500

Epoch 00395: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0562 - val_loss: 0.0255 - val_mse: 0.0255 - val_mae: 0.1290 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 396/500

Epoch 00396: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0559 - val_loss: 0.0253 - val_mse: 0.0253 - val_mae: 0.1283 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 397/500

Epoch 00397: val_loss improved from 0.02519 to 0.02519, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0544 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1281 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 398/500

Epoch 00398: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0565 - val_loss: 0.0253 - val_mse: 0.0253 - val_mae: 0.1284 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 399/500

Epoch 00399: val_loss did not improve from 0.02519
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0554 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1282 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 400/500

Epoch 00400: val_loss improved from 0.02519 to 0.02503, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0558 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1277 - lr: 1.0000e-05 - 122ms/epoch - 8ms/step
Epoch 401/500

Epoch 00401: val_loss improved from 0.02503 to 0.02499, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0577 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1276 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 402/500

Epoch 00402: val_loss did not improve from 0.02499
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1280 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 403/500

Epoch 00403: val_loss improved from 0.02499 to 0.02495, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0545 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1275 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 404/500

Epoch 00404: val_loss improved from 0.02495 to 0.02491, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0588 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1275 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 405/500

Epoch 00405: val_loss did not improve from 0.02491
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0562 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1277 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 406/500

Epoch 00406: val_loss did not improve from 0.02491
16/16 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0592 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1281 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 407/500

Epoch 00407: val_loss did not improve from 0.02491
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0579 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1280 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 408/500

Epoch 00408: val_loss did not improve from 0.02491
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0531 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1280 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 409/500

Epoch 00409: val_loss improved from 0.02491 to 0.02490, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0554 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1275 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 410/500

Epoch 00410: val_loss improved from 0.02490 to 0.02485, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0550 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1274 - lr: 1.0000e-05 - 106ms/epoch - 7ms/step
Epoch 411/500

Epoch 00411: val_loss improved from 0.02485 to 0.02478, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0564 - val_loss: 0.0248 - val_mse: 0.0248 - val_mae: 0.1271 - lr: 1.0000e-05 - 136ms/epoch - 8ms/step
Epoch 412/500

Epoch 00412: val_loss improved from 0.02478 to 0.02457, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0580 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1265 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 413/500

Epoch 00413: val_loss improved from 0.02457 to 0.02433, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0572 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1258 - lr: 1.0000e-05 - 118ms/epoch - 7ms/step
Epoch 414/500

Epoch 00414: val_loss improved from 0.02433 to 0.02427, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0551 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1256 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 415/500

Epoch 00415: val_loss improved from 0.02427 to 0.02416, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0576 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1252 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 416/500

Epoch 00416: val_loss improved from 0.02416 to 0.02416, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0567 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1252 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 417/500

Epoch 00417: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0525 - val_loss: 0.0244 - val_mse: 0.0244 - val_mae: 0.1260 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 418/500

Epoch 00418: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0540 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1267 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 419/500

Epoch 00419: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0562 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1268 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 420/500

Epoch 00420: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0537 - val_loss: 0.0248 - val_mse: 0.0248 - val_mae: 0.1275 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 421/500

Epoch 00421: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0537 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1286 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 422/500

Epoch 00422: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0560 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1286 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 423/500

Epoch 00423: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0565 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1285 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 424/500

Epoch 00424: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0523 - val_loss: 0.0251 - val_mse: 0.0251 - val_mae: 0.1285 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 425/500

Epoch 00425: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0574 - val_loss: 0.0252 - val_mse: 0.0252 - val_mae: 0.1288 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 426/500

Epoch 00426: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1282 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 427/500

Epoch 00427: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0556 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1283 - lr: 1.0000e-05 - 78ms/epoch - 5ms/step
Epoch 428/500

Epoch 00428: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0561 - val_loss: 0.0250 - val_mse: 0.0250 - val_mae: 0.1281 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 429/500

Epoch 00429: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0542 - val_loss: 0.0249 - val_mse: 0.0249 - val_mae: 0.1280 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 430/500

Epoch 00430: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0536 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1275 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 431/500

Epoch 00431: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0545 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1274 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 432/500

Epoch 00432: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0564 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1275 - lr: 1.0000e-05 - 76ms/epoch - 5ms/step
Epoch 433/500

Epoch 00433: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0540 - val_loss: 0.0248 - val_mse: 0.0248 - val_mae: 0.1278 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 434/500

Epoch 00434: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0547 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1275 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 435/500

Epoch 00435: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0557 - val_loss: 0.0247 - val_mse: 0.0247 - val_mae: 0.1273 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 436/500

Epoch 00436: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0549 - val_loss: 0.0245 - val_mse: 0.0245 - val_mae: 0.1268 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 437/500

Epoch 00437: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0553 - val_loss: 0.0244 - val_mse: 0.0244 - val_mae: 0.1264 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 438/500

Epoch 00438: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0557 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1262 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 439/500

Epoch 00439: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0578 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1263 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 440/500

Epoch 00440: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0547 - val_loss: 0.0245 - val_mse: 0.0245 - val_mae: 0.1268 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 441/500

Epoch 00441: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0244 - val_mse: 0.0244 - val_mae: 0.1267 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 442/500

Epoch 00442: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0541 - val_loss: 0.0245 - val_mse: 0.0245 - val_mae: 0.1269 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 443/500

Epoch 00443: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0552 - val_loss: 0.0245 - val_mse: 0.0245 - val_mae: 0.1269 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 444/500

Epoch 00444: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0563 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1265 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 445/500

Epoch 00445: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0530 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1262 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 446/500

Epoch 00446: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0560 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1264 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 447/500

Epoch 00447: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0563 - val_loss: 0.0243 - val_mse: 0.0243 - val_mae: 0.1265 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 448/500

Epoch 00448: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0558 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1261 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 449/500

Epoch 00449: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0533 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1260 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 450/500

Epoch 00450: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0564 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1262 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 451/500

Epoch 00451: val_loss did not improve from 0.02416
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0540 - val_loss: 0.0242 - val_mse: 0.0242 - val_mae: 0.1261 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 452/500

Epoch 00452: val_loss improved from 0.02416 to 0.02412, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0553 - val_loss: 0.0241 - val_mse: 0.0241 - val_mae: 0.1260 - lr: 1.0000e-05 - 114ms/epoch - 7ms/step
Epoch 453/500

Epoch 00453: val_loss improved from 0.02412 to 0.02397, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0534 - val_loss: 0.0240 - val_mse: 0.0240 - val_mae: 0.1256 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 454/500

Epoch 00454: val_loss improved from 0.02397 to 0.02385, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0556 - val_loss: 0.0238 - val_mse: 0.0238 - val_mae: 0.1252 - lr: 1.0000e-05 - 145ms/epoch - 9ms/step
Epoch 455/500

Epoch 00455: val_loss did not improve from 0.02385
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0550 - val_loss: 0.0240 - val_mse: 0.0240 - val_mae: 0.1255 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 456/500

Epoch 00456: val_loss did not improve from 0.02385
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0552 - val_loss: 0.0239 - val_mse: 0.0239 - val_mae: 0.1253 - lr: 1.0000e-05 - 75ms/epoch - 5ms/step
Epoch 457/500

Epoch 00457: val_loss improved from 0.02385 to 0.02369, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0576 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1248 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 458/500

Epoch 00458: val_loss improved from 0.02369 to 0.02366, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0553 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1246 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 459/500

Epoch 00459: val_loss improved from 0.02366 to 0.02365, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0544 - val_loss: 0.0236 - val_mse: 0.0236 - val_mae: 0.1246 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 460/500

Epoch 00460: val_loss improved from 0.02365 to 0.02351, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0536 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1242 - lr: 1.0000e-05 - 138ms/epoch - 9ms/step
Epoch 461/500

Epoch 00461: val_loss improved from 0.02351 to 0.02348, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0551 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1241 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 462/500

Epoch 00462: val_loss did not improve from 0.02348
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0541 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1241 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 463/500

Epoch 00463: val_loss improved from 0.02348 to 0.02339, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0546 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1238 - lr: 1.0000e-05 - 89ms/epoch - 6ms/step
Epoch 464/500

Epoch 00464: val_loss improved from 0.02339 to 0.02324, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0551 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1234 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 465/500

Epoch 00465: val_loss did not improve from 0.02324
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0549 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1235 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 466/500

Epoch 00466: val_loss improved from 0.02324 to 0.02314, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0542 - val_loss: 0.0231 - val_mse: 0.0231 - val_mae: 0.1231 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 467/500

Epoch 00467: val_loss improved from 0.02314 to 0.02295, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0537 - val_loss: 0.0230 - val_mse: 0.0230 - val_mae: 0.1225 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 468/500

Epoch 00468: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0526 - val_loss: 0.0230 - val_mse: 0.0230 - val_mae: 0.1227 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 469/500

Epoch 00469: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0527 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1234 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 470/500

Epoch 00470: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0522 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1241 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 471/500

Epoch 00471: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0529 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1243 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 472/500

Epoch 00472: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0554 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1241 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 473/500

Epoch 00473: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0552 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1242 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 474/500

Epoch 00474: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0527 - val_loss: 0.0234 - val_mse: 0.0234 - val_mae: 0.1240 - lr: 1.0000e-05 - 88ms/epoch - 5ms/step
Epoch 475/500

Epoch 00475: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0547 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1238 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 476/500

Epoch 00476: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0572 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1239 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 477/500

Epoch 00477: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0547 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1245 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 478/500

Epoch 00478: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0572 - val_loss: 0.0235 - val_mse: 0.0235 - val_mae: 0.1245 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 479/500

Epoch 00479: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0545 - val_loss: 0.0233 - val_mse: 0.0233 - val_mae: 0.1240 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 480/500

Epoch 00480: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0539 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1236 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 481/500

Epoch 00481: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0526 - val_loss: 0.0231 - val_mse: 0.0231 - val_mae: 0.1234 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 482/500

Epoch 00482: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0532 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1235 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 483/500

Epoch 00483: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0532 - val_loss: 0.0232 - val_mse: 0.0232 - val_mae: 0.1235 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 484/500

Epoch 00484: val_loss did not improve from 0.02295
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0551 - val_loss: 0.0230 - val_mse: 0.0230 - val_mae: 0.1229 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 485/500

Epoch 00485: val_loss improved from 0.02295 to 0.02291, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0539 - val_loss: 0.0229 - val_mse: 0.0229 - val_mae: 0.1228 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
Epoch 486/500

Epoch 00486: val_loss did not improve from 0.02291
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0543 - val_loss: 0.0230 - val_mse: 0.0230 - val_mae: 0.1231 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 487/500

Epoch 00487: val_loss improved from 0.02291 to 0.02280, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0526 - val_loss: 0.0228 - val_mse: 0.0228 - val_mae: 0.1224 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 488/500

Epoch 00488: val_loss improved from 0.02280 to 0.02264, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0555 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1219 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 489/500

Epoch 00489: val_loss improved from 0.02264 to 0.02257, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0540 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1217 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 490/500

Epoch 00490: val_loss did not improve from 0.02257
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0536 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1218 - lr: 1.0000e-05 - 88ms/epoch - 6ms/step
Epoch 491/500

Epoch 00491: val_loss did not improve from 0.02257
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0536 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1218 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 492/500

Epoch 00492: val_loss did not improve from 0.02257
16/16 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0493 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1220 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 493/500

Epoch 00493: val_loss did not improve from 0.02257
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0521 - val_loss: 0.0226 - val_mse: 0.0226 - val_mae: 0.1220 - lr: 1.0000e-05 - 79ms/epoch - 5ms/step
Epoch 494/500

Epoch 00494: val_loss improved from 0.02257 to 0.02240, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0526 - val_loss: 0.0224 - val_mse: 0.0224 - val_mae: 0.1212 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 495/500

Epoch 00495: val_loss improved from 0.02240 to 0.02230, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0542 - val_loss: 0.0223 - val_mse: 0.0223 - val_mae: 0.1209 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 496/500

Epoch 00496: val_loss improved from 0.02230 to 0.02204, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0546 - val_loss: 0.0220 - val_mse: 0.0220 - val_mae: 0.1201 - lr: 1.0000e-05 - 104ms/epoch - 7ms/step
Epoch 497/500

Epoch 00497: val_loss did not improve from 0.02204
16/16 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0551 - val_loss: 0.0221 - val_mse: 0.0221 - val_mae: 0.1202 - lr: 1.0000e-05 - 81ms/epoch - 5ms/step
Epoch 498/500

Epoch 00498: val_loss did not improve from 0.02204
16/16 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0532 - val_loss: 0.0221 - val_mse: 0.0221 - val_mae: 0.1203 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 499/500

Epoch 00499: val_loss improved from 0.02204 to 0.02197, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0548 - val_loss: 0.0220 - val_mse: 0.0220 - val_mae: 0.1199 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 500/500

Epoch 00500: val_loss improved from 0.02197 to 0.02183, saving model to LSTM7.h5
16/16 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0540 - val_loss: 0.0218 - val_mse: 0.0218 - val_mae: 0.1195 - lr: 1.0000e-05 - 117ms/epoch - 7ms/step
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.53 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.34 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14597.576, Time=5.56 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=8.44 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15338.693, Time=10.98 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15153.472, Time=25.66 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17112.658, Time=15.33 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.587, Time=10.00 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15106.216, Time=14.12 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-12251.715, Time=35.41 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 134.382 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8588.329
Date:                Sun, 12 Dec 2021   AIC                         -17112.658
Time:                        18:30:04   BIC                         -16962.551
Sample:                             0   HQIC                        -17055.011
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          -4.53e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x2         -4.512e-09   3.25e-06     -0.001      0.999   -6.38e-06    6.37e-06
x3         -4.538e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x4             1.0000   3.26e-06   3.07e+05      0.000       1.000       1.000
x5         -4.105e-09   3.11e-06     -0.001      0.999    -6.1e-06    6.09e-06
x6         -1.488e-08   5.45e-06     -0.003      0.998   -1.07e-05    1.07e-05
x7         -4.481e-09   3.24e-06     -0.001      0.999   -6.36e-06    6.36e-06
x8         -4.365e-09    3.2e-06     -0.001      0.999   -6.29e-06    6.28e-06
x9         -4.628e-10   8.38e-07     -0.001      1.000   -1.64e-06    1.64e-06
x10        -7.326e-10    1.3e-06     -0.001      1.000   -2.55e-06    2.54e-06
x11        -4.347e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x12        -4.345e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x13         -4.52e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x14        -3.586e-08      9e-06     -0.004      0.997   -1.77e-05    1.76e-05
x15        -3.757e-09   2.98e-06     -0.001      0.999   -5.84e-06    5.83e-06
x16         -1.24e-08   5.36e-06     -0.002      0.998   -1.05e-05    1.05e-05
x17        -4.515e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x18        -2.632e-10   7.07e-07     -0.000      1.000   -1.39e-06    1.39e-06
x19        -4.642e-09    3.3e-06     -0.001      0.999   -6.47e-06    6.46e-06
x20        -3.919e-10   6.91e-07     -0.001      1.000   -1.36e-06    1.35e-06
x21         -7.69e-09   4.13e-06     -0.002      0.999   -8.11e-06    8.09e-06
x22        -6.998e-12   2.69e-13    -25.970      0.000   -7.53e-12   -6.47e-12
x23         -1.81e-10   2.22e-12    -81.582      0.000   -1.85e-10   -1.77e-10
x24        -4.955e-08    8.9e-06     -0.006      0.996   -1.75e-05    1.74e-05
x25        -4.901e-08    8.4e-06     -0.006      0.995   -1.65e-05    1.64e-05
x26        -6.446e-08    1.2e-05     -0.005      0.996   -2.37e-05    2.35e-05
x27         -5.73e-08   1.14e-05     -0.005      0.996   -2.24e-05    2.23e-05
x28        -2.997e-08   8.22e-06     -0.004      0.997   -1.61e-05    1.61e-05
x29        -3.486e-08   8.89e-06     -0.004      0.997   -1.75e-05    1.74e-05
ma.L1         -1.3902   3.62e-10  -3.84e+09      0.000      -1.390      -1.390
ma.L2          0.4033   3.72e-10   1.08e+09      0.000       0.403       0.403
sigma2      8.541e-11   6.95e-11      1.229      0.219   -5.08e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  66.92   Jarque-Bera (JB):           6039240.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                            12.14
Prob(H) (two-sided):                  0.00   Kurtosis:                       426.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.94e+30. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.30534, saving model to LSTM7.h5
17/17 - 2s - loss: 0.1565 - mse: 0.1565 - mae: 0.3274 - val_loss: 0.3053 - val_mse: 0.3053 - val_mae: 0.5221 - lr: 0.0010 - 2s/epoch - 125ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.30534 to 0.25749, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0395 - mse: 0.0395 - mae: 0.1562 - val_loss: 0.2575 - val_mse: 0.2575 - val_mae: 0.4783 - lr: 0.0010 - 105ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.25749 to 0.23086, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0299 - mse: 0.0299 - mae: 0.1376 - val_loss: 0.2309 - val_mse: 0.2309 - val_mae: 0.4531 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.23086 to 0.22231, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0298 - mse: 0.0298 - mae: 0.1382 - val_loss: 0.2223 - val_mse: 0.2223 - val_mae: 0.4443 - lr: 0.0010 - 93ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.22231 to 0.19973, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0242 - mse: 0.0242 - mae: 0.1241 - val_loss: 0.1997 - val_mse: 0.1997 - val_mae: 0.4202 - lr: 0.0010 - 91ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.19973 to 0.17981, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0206 - mse: 0.0206 - mae: 0.1142 - val_loss: 0.1798 - val_mse: 0.1798 - val_mae: 0.3972 - lr: 0.0010 - 118ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.17981 to 0.15455, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0186 - mse: 0.0186 - mae: 0.1092 - val_loss: 0.1545 - val_mse: 0.1545 - val_mae: 0.3666 - lr: 0.0010 - 97ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.15455
17/17 - 0s - loss: 0.0159 - mse: 0.0159 - mae: 0.1014 - val_loss: 0.1656 - val_mse: 0.1656 - val_mae: 0.3820 - lr: 0.0010 - 93ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.15455
17/17 - 0s - loss: 0.0138 - mse: 0.0138 - mae: 0.0929 - val_loss: 0.1584 - val_mse: 0.1584 - val_mae: 0.3740 - lr: 0.0010 - 84ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss improved from 0.15455 to 0.13938, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0111 - mse: 0.0111 - mae: 0.0830 - val_loss: 0.1394 - val_mse: 0.1394 - val_mae: 0.3495 - lr: 0.0010 - 120ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.13938 to 0.13594, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0119 - mse: 0.0119 - mae: 0.0867 - val_loss: 0.1359 - val_mse: 0.1359 - val_mae: 0.3454 - lr: 0.0010 - 101ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.13594 to 0.11705, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0112 - mse: 0.0112 - mae: 0.0822 - val_loss: 0.1171 - val_mse: 0.1171 - val_mae: 0.3192 - lr: 0.0010 - 112ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.11705 to 0.10633, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0095 - mse: 0.0095 - mae: 0.0773 - val_loss: 0.1063 - val_mse: 0.1063 - val_mae: 0.3032 - lr: 0.0010 - 109ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss improved from 0.10633 to 0.09558, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0089 - mse: 0.0089 - mae: 0.0741 - val_loss: 0.0956 - val_mse: 0.0956 - val_mae: 0.2868 - lr: 0.0010 - 118ms/epoch - 7ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.09558
17/17 - 0s - loss: 0.0083 - mse: 0.0083 - mae: 0.0710 - val_loss: 0.1009 - val_mse: 0.1009 - val_mae: 0.2964 - lr: 0.0010 - 83ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.09558 to 0.08888, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0706 - val_loss: 0.0889 - val_mse: 0.0889 - val_mae: 0.2764 - lr: 0.0010 - 100ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss improved from 0.08888 to 0.08816, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0681 - val_loss: 0.0882 - val_mse: 0.0882 - val_mae: 0.2755 - lr: 0.0010 - 110ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.08816
17/17 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0655 - val_loss: 0.0902 - val_mse: 0.0902 - val_mae: 0.2802 - lr: 0.0010 - 77ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss improved from 0.08816 to 0.07791, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0647 - val_loss: 0.0779 - val_mse: 0.0779 - val_mae: 0.2589 - lr: 0.0010 - 118ms/epoch - 7ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.07791
17/17 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0663 - val_loss: 0.0814 - val_mse: 0.0814 - val_mae: 0.2659 - lr: 0.0010 - 83ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.07791
17/17 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0607 - val_loss: 0.0787 - val_mse: 0.0787 - val_mae: 0.2605 - lr: 0.0010 - 81ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.07791
17/17 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0586 - val_loss: 0.0842 - val_mse: 0.0842 - val_mae: 0.2709 - lr: 0.0010 - 90ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss improved from 0.07791 to 0.07391, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0603 - val_loss: 0.0739 - val_mse: 0.0739 - val_mae: 0.2531 - lr: 0.0010 - 104ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.07391
17/17 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0637 - val_loss: 0.0862 - val_mse: 0.0862 - val_mae: 0.2757 - lr: 0.0010 - 100ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.07391
17/17 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0578 - val_loss: 0.0755 - val_mse: 0.0755 - val_mae: 0.2557 - lr: 0.0010 - 99ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss improved from 0.07391 to 0.06662, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0541 - val_loss: 0.0666 - val_mse: 0.0666 - val_mae: 0.2391 - lr: 0.0010 - 102ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.06662
17/17 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0557 - val_loss: 0.0728 - val_mse: 0.0728 - val_mae: 0.2523 - lr: 0.0010 - 96ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.06662 to 0.06080, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0578 - val_loss: 0.0608 - val_mse: 0.0608 - val_mae: 0.2289 - lr: 0.0010 - 102ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.06080
17/17 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0564 - val_loss: 0.0626 - val_mse: 0.0626 - val_mae: 0.2327 - lr: 0.0010 - 80ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.06080
17/17 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0519 - val_loss: 0.0635 - val_mse: 0.0635 - val_mae: 0.2345 - lr: 0.0010 - 86ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss improved from 0.06080 to 0.04957, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0525 - val_loss: 0.0496 - val_mse: 0.0496 - val_mae: 0.2046 - lr: 0.0010 - 106ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04957
17/17 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0573 - val_loss: 0.0550 - val_mse: 0.0550 - val_mae: 0.2182 - lr: 0.0010 - 88ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss improved from 0.04957 to 0.04416, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0575 - val_loss: 0.0442 - val_mse: 0.0442 - val_mae: 0.1929 - lr: 0.0010 - 119ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04416
17/17 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0596 - val_loss: 0.0517 - val_mse: 0.0517 - val_mae: 0.2105 - lr: 0.0010 - 106ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04416
17/17 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0511 - val_loss: 0.0574 - val_mse: 0.0574 - val_mae: 0.2232 - lr: 0.0010 - 84ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04416
17/17 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0564 - val_loss: 0.0517 - val_mse: 0.0517 - val_mae: 0.2116 - lr: 0.0010 - 85ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04416
17/17 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0576 - val_loss: 0.0497 - val_mse: 0.0497 - val_mae: 0.2078 - lr: 0.0010 - 89ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss improved from 0.04416 to 0.03520, saving model to LSTM7.h5
17/17 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0595 - val_loss: 0.0352 - val_mse: 0.0352 - val_mae: 0.1705 - lr: 0.0010 - 114ms/epoch - 7ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0565 - val_loss: 0.0544 - val_mse: 0.0544 - val_mae: 0.2172 - lr: 0.0010 - 88ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0551 - val_loss: 0.0683 - val_mse: 0.0683 - val_mae: 0.2465 - lr: 0.0010 - 77ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0637 - val_loss: 0.0464 - val_mse: 0.0464 - val_mae: 0.2005 - lr: 0.0010 - 84ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0608 - val_loss: 0.0416 - val_mse: 0.0416 - val_mae: 0.1889 - lr: 0.0010 - 88ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00043: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0602 - val_loss: 0.0483 - val_mse: 0.0483 - val_mae: 0.2043 - lr: 0.0010 - 95ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0501 - val_loss: 0.0502 - val_mse: 0.0502 - val_mae: 0.2088 - lr: 1.0000e-04 - 87ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0475 - val_loss: 0.0518 - val_mse: 0.0518 - val_mae: 0.2126 - lr: 1.0000e-04 - 80ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0473 - val_loss: 0.0516 - val_mse: 0.0516 - val_mae: 0.2120 - lr: 1.0000e-04 - 84ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0467 - val_loss: 0.0504 - val_mse: 0.0504 - val_mae: 0.2096 - lr: 1.0000e-04 - 81ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00048: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0466 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.2093 - lr: 1.0000e-04 - 97ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.2092 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0445 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.2092 - lr: 1.0000e-05 - 80ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0448 - val_loss: 0.0502 - val_mse: 0.0502 - val_mae: 0.2091 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0459 - val_loss: 0.0502 - val_mse: 0.0502 - val_mae: 0.2091 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00053: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0457 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.2093 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0445 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.2092 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0449 - val_loss: 0.0502 - val_mse: 0.0502 - val_mae: 0.2092 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0472 - val_loss: 0.0502 - val_mse: 0.0502 - val_mae: 0.2090 - lr: 1.0000e-05 - 87ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.2093 - lr: 1.0000e-05 - 89ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0461 - val_loss: 0.0504 - val_mse: 0.0504 - val_mae: 0.2096 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0458 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2097 - lr: 1.0000e-05 - 108ms/epoch - 6ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0449 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2099 - lr: 1.0000e-05 - 113ms/epoch - 7ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0451 - val_loss: 0.0507 - val_mse: 0.0507 - val_mae: 0.2101 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0451 - val_loss: 0.0507 - val_mse: 0.0507 - val_mae: 0.2102 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0463 - val_loss: 0.0507 - val_mse: 0.0507 - val_mae: 0.2102 - lr: 1.0000e-05 - 86ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0463 - val_loss: 0.0507 - val_mse: 0.0507 - val_mae: 0.2102 - lr: 1.0000e-05 - 83ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0448 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2101 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0469 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2099 - lr: 1.0000e-05 - 93ms/epoch - 5ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0448 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0467 - val_loss: 0.0507 - val_mse: 0.0507 - val_mae: 0.2102 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0466 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2098 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0453 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2098 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0445 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0462 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 73/500

Epoch 00073: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0443 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2098 - lr: 1.0000e-05 - 93ms/epoch - 5ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0467 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2101 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0466 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0458 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2098 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0452 - val_loss: 0.0504 - val_mse: 0.0504 - val_mae: 0.2097 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0465 - val_loss: 0.0504 - val_mse: 0.0504 - val_mae: 0.2096 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0459 - val_loss: 0.0504 - val_mse: 0.0504 - val_mae: 0.2096 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0449 - val_loss: 0.0504 - val_mse: 0.0504 - val_mae: 0.2097 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 81/500

Epoch 00081: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0464 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0465 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2097 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0464 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 84ms/epoch - 5ms/step
Epoch 84/500

Epoch 00084: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0431 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2102 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 85/500

Epoch 00085: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0443 - val_loss: 0.0506 - val_mse: 0.0506 - val_mae: 0.2100 - lr: 1.0000e-05 - 93ms/epoch - 5ms/step
Epoch 86/500

Epoch 00086: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0463 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2099 - lr: 1.0000e-05 - 82ms/epoch - 5ms/step
Epoch 87/500

Epoch 00087: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0464 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2099 - lr: 1.0000e-05 - 91ms/epoch - 5ms/step
Epoch 88/500

Epoch 00088: val_loss did not improve from 0.03520
17/17 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0452 - val_loss: 0.0505 - val_mse: 0.0505 - val_mae: 0.2100 - lr: 1.0000e-05 - 85ms/epoch - 5ms/step
Epoch 00088: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 42.30488040231578 
RMSE:	 6.504220199402522 
MAPE:	 5.010195929360332
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.776, Time=3.29 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.586, Time=5.34 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16271.755, Time=7.16 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.586, Time=8.20 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15152.908, Time=10.90 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14481.105, Time=12.97 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16088.109, Time=20.81 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.021, Time=6.25 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.615, Time=3.78 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=7.55 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=18.60 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.981, Time=4.57 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.666, Time=4.55 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 114.014 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        18:36:11   BIC                         -16911.965
Sample:                             0   HQIC                        -17010.203
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -2.8e-10   6.02e-05  -4.65e-06      1.000      -0.000       0.000
x2         -2.817e-10   6.04e-05  -4.66e-06      1.000      -0.000       0.000
x3         -2.805e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x4             1.0000   6.03e-05   1.66e+04      0.000       1.000       1.000
x5           -2.6e-10    5.8e-05  -4.48e-06      1.000      -0.000       0.000
x6         -1.389e-09      0.000  -1.08e-05      1.000      -0.000       0.000
x7         -2.789e-10   6.01e-05  -4.64e-06      1.000      -0.000       0.000
x8         -2.763e-10   5.99e-05  -4.62e-06      1.000      -0.000       0.000
x9         -2.224e-12    1.6e-06  -1.39e-06      1.000   -3.13e-06    3.13e-06
x10        -1.345e-10   4.12e-05  -3.26e-06      1.000   -8.08e-05    8.08e-05
x11          -2.9e-10   6.12e-05  -4.74e-06      1.000      -0.000       0.000
x12        -2.602e-10   5.82e-05  -4.47e-06      1.000      -0.000       0.000
x13        -2.807e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x14         -1.87e-09      0.000   -1.2e-05      1.000      -0.000       0.000
x15        -2.844e-10   6.05e-05   -4.7e-06      1.000      -0.000       0.000
x16        -7.962e-11    3.2e-05  -2.48e-06      1.000   -6.28e-05    6.28e-05
x17        -2.445e-10   5.61e-05  -4.36e-06      1.000      -0.000       0.000
x18          -6.4e-10   9.15e-05  -6.99e-06      1.000      -0.000       0.000
x19        -2.923e-10   6.14e-05  -4.76e-06      1.000      -0.000       0.000
x20        -4.336e-10   7.41e-05  -5.86e-06      1.000      -0.000       0.000
x21         -4.55e-10    7.5e-05  -6.07e-06      1.000      -0.000       0.000
x22        -3.587e-13   1.42e-11     -0.025      0.980   -2.82e-11    2.75e-11
x23        -1.088e-11   9.56e-11     -0.114      0.909   -1.98e-10    1.76e-10
x24        -2.146e-09      0.000  -1.63e-05      1.000      -0.000       0.000
x25        -1.637e-09      0.000  -1.35e-05      1.000      -0.000       0.000
x26        -3.147e-09      0.000  -1.56e-05      1.000      -0.000       0.000
x27         -2.58e-09      0.000  -1.41e-05      1.000      -0.000       0.000
x28        -2.444e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x29        -1.666e-09      0.000  -1.13e-05      1.000      -0.000       0.000
ar.L1         -0.4923    5.1e-10  -9.65e+08      0.000      -0.492      -0.492
ar.L2         -0.1923   2.96e-10  -6.49e+08      0.000      -0.192      -0.192
ar.L3         -0.0462    1.4e-10  -3.29e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.16e-09  -6.12e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  54.06   Jarque-Bera (JB):           4126495.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.48
Prob(H) (two-sided):                  0.00   Kurtosis:                       353.58
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 2.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.85386, saving model to LSTM7.h5
10/10 - 2s - loss: 0.7346 - mse: 0.7346 - mae: 0.7356 - val_loss: 0.8539 - val_mse: 0.8539 - val_mae: 0.9053 - lr: 0.0010 - 2s/epoch - 218ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.85386 to 0.57893, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0770 - mse: 0.0770 - mae: 0.2318 - val_loss: 0.5789 - val_mse: 0.5789 - val_mae: 0.7425 - lr: 0.0010 - 75ms/epoch - 8ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.57893 to 0.40598, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0758 - mse: 0.0758 - mae: 0.2373 - val_loss: 0.4060 - val_mse: 0.4060 - val_mae: 0.6181 - lr: 0.0010 - 92ms/epoch - 9ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.40598 to 0.29908, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0418 - mse: 0.0418 - mae: 0.1718 - val_loss: 0.2991 - val_mse: 0.2991 - val_mae: 0.5267 - lr: 0.0010 - 95ms/epoch - 9ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.29908 to 0.23737, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0252 - mse: 0.0252 - mae: 0.1260 - val_loss: 0.2374 - val_mse: 0.2374 - val_mae: 0.4658 - lr: 0.0010 - 72ms/epoch - 7ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.23737 to 0.20982, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0257 - mse: 0.0257 - mae: 0.1275 - val_loss: 0.2098 - val_mse: 0.2098 - val_mae: 0.4361 - lr: 0.0010 - 117ms/epoch - 12ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.20982 to 0.18859, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0218 - mse: 0.0218 - mae: 0.1176 - val_loss: 0.1886 - val_mse: 0.1886 - val_mae: 0.4119 - lr: 0.0010 - 92ms/epoch - 9ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.18859 to 0.17341, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0192 - mse: 0.0192 - mae: 0.1121 - val_loss: 0.1734 - val_mse: 0.1734 - val_mae: 0.3936 - lr: 0.0010 - 101ms/epoch - 10ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.17341 to 0.16995, saving model to LSTM7.h5
10/10 - 0s - loss: 0.0199 - mse: 0.0199 - mae: 0.1131 - val_loss: 0.1699 - val_mse: 0.1699 - val_mae: 0.3894 - lr: 0.0010 - 97ms/epoch - 10ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0174 - mse: 0.0174 - mae: 0.1035 - val_loss: 0.1730 - val_mse: 0.1730 - val_mae: 0.3934 - lr: 0.0010 - 60ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0164 - mse: 0.0164 - mae: 0.1036 - val_loss: 0.1854 - val_mse: 0.1854 - val_mae: 0.4085 - lr: 0.0010 - 68ms/epoch - 7ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0156 - mse: 0.0156 - mae: 0.0984 - val_loss: 0.1808 - val_mse: 0.1808 - val_mae: 0.4030 - lr: 0.0010 - 73ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0136 - mse: 0.0136 - mae: 0.0937 - val_loss: 0.1780 - val_mse: 0.1780 - val_mae: 0.3994 - lr: 0.0010 - 72ms/epoch - 7ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00014: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0146 - mse: 0.0146 - mae: 0.0952 - val_loss: 0.1813 - val_mse: 0.1813 - val_mae: 0.4035 - lr: 0.0010 - 67ms/epoch - 7ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0141 - mse: 0.0141 - mae: 0.0942 - val_loss: 0.1807 - val_mse: 0.1807 - val_mae: 0.4028 - lr: 1.0000e-04 - 77ms/epoch - 8ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0143 - mse: 0.0143 - mae: 0.0947 - val_loss: 0.1810 - val_mse: 0.1810 - val_mae: 0.4031 - lr: 1.0000e-04 - 76ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0129 - mse: 0.0129 - mae: 0.0924 - val_loss: 0.1803 - val_mse: 0.1803 - val_mae: 0.4024 - lr: 1.0000e-04 - 83ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0126 - mse: 0.0126 - mae: 0.0895 - val_loss: 0.1790 - val_mse: 0.1790 - val_mae: 0.4007 - lr: 1.0000e-04 - 86ms/epoch - 9ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00019: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0131 - mse: 0.0131 - mae: 0.0911 - val_loss: 0.1779 - val_mse: 0.1779 - val_mae: 0.3994 - lr: 1.0000e-04 - 63ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0133 - mse: 0.0133 - mae: 0.0925 - val_loss: 0.1777 - val_mse: 0.1777 - val_mae: 0.3991 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0892 - val_loss: 0.1774 - val_mse: 0.1774 - val_mae: 0.3988 - lr: 1.0000e-05 - 60ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0135 - mse: 0.0135 - mae: 0.0925 - val_loss: 0.1773 - val_mse: 0.1773 - val_mae: 0.3986 - lr: 1.0000e-05 - 65ms/epoch - 7ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0142 - mse: 0.0142 - mae: 0.0963 - val_loss: 0.1770 - val_mse: 0.1770 - val_mae: 0.3983 - lr: 1.0000e-05 - 61ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00024: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0896 - val_loss: 0.1768 - val_mse: 0.1768 - val_mae: 0.3980 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0123 - mse: 0.0123 - mae: 0.0889 - val_loss: 0.1767 - val_mse: 0.1767 - val_mae: 0.3979 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0122 - mse: 0.0122 - mae: 0.0880 - val_loss: 0.1766 - val_mse: 0.1766 - val_mae: 0.3978 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0116 - mse: 0.0116 - mae: 0.0855 - val_loss: 0.1765 - val_mse: 0.1765 - val_mae: 0.3977 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0130 - mse: 0.0130 - mae: 0.0897 - val_loss: 0.1763 - val_mse: 0.1763 - val_mae: 0.3974 - lr: 1.0000e-05 - 63ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0121 - mse: 0.0121 - mae: 0.0880 - val_loss: 0.1762 - val_mse: 0.1762 - val_mae: 0.3972 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0129 - mse: 0.0129 - mae: 0.0908 - val_loss: 0.1760 - val_mse: 0.1760 - val_mae: 0.3970 - lr: 1.0000e-05 - 61ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0129 - mse: 0.0129 - mae: 0.0895 - val_loss: 0.1758 - val_mse: 0.1758 - val_mae: 0.3968 - lr: 1.0000e-05 - 58ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0891 - val_loss: 0.1756 - val_mse: 0.1756 - val_mae: 0.3966 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0132 - mse: 0.0132 - mae: 0.0908 - val_loss: 0.1755 - val_mse: 0.1755 - val_mae: 0.3965 - lr: 1.0000e-05 - 66ms/epoch - 7ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0137 - mse: 0.0137 - mae: 0.0942 - val_loss: 0.1754 - val_mse: 0.1754 - val_mae: 0.3963 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0130 - mse: 0.0130 - mae: 0.0905 - val_loss: 0.1753 - val_mse: 0.1753 - val_mae: 0.3962 - lr: 1.0000e-05 - 65ms/epoch - 7ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0125 - mse: 0.0125 - mae: 0.0882 - val_loss: 0.1753 - val_mse: 0.1753 - val_mae: 0.3961 - lr: 1.0000e-05 - 65ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0122 - mse: 0.0122 - mae: 0.0883 - val_loss: 0.1751 - val_mse: 0.1751 - val_mae: 0.3959 - lr: 1.0000e-05 - 56ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0124 - mse: 0.0124 - mae: 0.0875 - val_loss: 0.1749 - val_mse: 0.1749 - val_mae: 0.3957 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0137 - mse: 0.0137 - mae: 0.0911 - val_loss: 0.1747 - val_mse: 0.1747 - val_mae: 0.3954 - lr: 1.0000e-05 - 83ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0133 - mse: 0.0133 - mae: 0.0913 - val_loss: 0.1744 - val_mse: 0.1744 - val_mae: 0.3951 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0124 - mse: 0.0124 - mae: 0.0887 - val_loss: 0.1742 - val_mse: 0.1742 - val_mae: 0.3949 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0117 - mse: 0.0117 - mae: 0.0859 - val_loss: 0.1742 - val_mse: 0.1742 - val_mae: 0.3948 - lr: 1.0000e-05 - 82ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0137 - mse: 0.0137 - mae: 0.0927 - val_loss: 0.1742 - val_mse: 0.1742 - val_mae: 0.3949 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0136 - mse: 0.0136 - mae: 0.0925 - val_loss: 0.1742 - val_mse: 0.1742 - val_mae: 0.3948 - lr: 1.0000e-05 - 92ms/epoch - 9ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0135 - mse: 0.0135 - mae: 0.0925 - val_loss: 0.1742 - val_mse: 0.1742 - val_mae: 0.3948 - lr: 1.0000e-05 - 87ms/epoch - 9ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0897 - val_loss: 0.1740 - val_mse: 0.1740 - val_mae: 0.3946 - lr: 1.0000e-05 - 61ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0143 - mse: 0.0143 - mae: 0.0941 - val_loss: 0.1740 - val_mse: 0.1740 - val_mae: 0.3945 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0129 - mse: 0.0129 - mae: 0.0911 - val_loss: 0.1740 - val_mse: 0.1740 - val_mae: 0.3946 - lr: 1.0000e-05 - 66ms/epoch - 7ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0126 - mse: 0.0126 - mae: 0.0901 - val_loss: 0.1740 - val_mse: 0.1740 - val_mae: 0.3946 - lr: 1.0000e-05 - 63ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0124 - mse: 0.0124 - mae: 0.0886 - val_loss: 0.1741 - val_mse: 0.1741 - val_mae: 0.3947 - lr: 1.0000e-05 - 57ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0128 - mse: 0.0128 - mae: 0.0892 - val_loss: 0.1739 - val_mse: 0.1739 - val_mae: 0.3944 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0130 - mse: 0.0130 - mae: 0.0903 - val_loss: 0.1734 - val_mse: 0.1734 - val_mae: 0.3939 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0905 - val_loss: 0.1731 - val_mse: 0.1731 - val_mae: 0.3934 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0888 - val_loss: 0.1727 - val_mse: 0.1727 - val_mae: 0.3930 - lr: 1.0000e-05 - 85ms/epoch - 9ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0131 - mse: 0.0131 - mae: 0.0902 - val_loss: 0.1728 - val_mse: 0.1728 - val_mae: 0.3930 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0137 - mse: 0.0137 - mae: 0.0907 - val_loss: 0.1727 - val_mse: 0.1727 - val_mae: 0.3930 - lr: 1.0000e-05 - 62ms/epoch - 6ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0131 - mse: 0.0131 - mae: 0.0901 - val_loss: 0.1725 - val_mse: 0.1725 - val_mae: 0.3927 - lr: 1.0000e-05 - 66ms/epoch - 7ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0130 - mse: 0.0130 - mae: 0.0907 - val_loss: 0.1726 - val_mse: 0.1726 - val_mae: 0.3928 - lr: 1.0000e-05 - 61ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.16995
10/10 - 0s - loss: 0.0132 - mse: 0.0132 - mae: 0.0906 - val_loss: 0.1724 - val_mse: 0.1724 - val_mae: 0.3926 - lr: 1.0000e-05 - 56ms/epoch - 6ms/step
Epoch 00059: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 42.30488040231578 
RMSE:	 6.504220199402522 
MAPE:	 5.010195929360332

DEMA
Prediction vs Close:		55.6% Accuracy
Prediction vs Prediction:	54.48% Accuracy
MSE:	 23.305922116020078 
RMSE:	 4.827620751055335 
MAPE:	 3.7452201197397774
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.104, Time=3.79 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.591, Time=5.51 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16779.655, Time=11.07 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.590, Time=8.68 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16989.430, Time=4.19 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-16990.286, Time=3.92 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.543, Time=4.21 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-16987.154, Time=4.39 sec
 ARIMA(2,3,0)(0,0,0)[0] intercept   : AIC=-16533.935, Time=16.56 sec

Best model:  ARIMA(2,3,0)(0,0,0)[0]          
Total fit time: 62.346 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(2, 3, 0)   Log Likelihood                8527.143
Date:                Sun, 12 Dec 2021   AIC                         -16990.286
Time:                        18:46:16   BIC                         -16840.179
Sample:                             0   HQIC                        -16932.639
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -1.1e-16        nan        nan        nan         nan         nan
x2         -3.811e-16         -0        inf      0.000   -3.81e-16   -3.81e-16
x3          8.776e-16   4.38e-27      2e+11      0.000    8.78e-16    8.78e-16
x4             1.0000   4.36e-27   2.29e+26      0.000       1.000       1.000
x5          6.686e-16   4.14e-27   1.61e+11      0.000    6.69e-16    6.69e-16
x6         -5.238e-17   9.44e-27  -5.55e+09      0.000   -5.24e-17   -5.24e-17
x7         -1.709e-16   4.37e-27  -3.91e+10      0.000   -1.71e-16   -1.71e-16
x8          1.439e-15   4.33e-27   3.32e+11      0.000    1.44e-15    1.44e-15
x9         -2.924e-16   5.73e-28   -5.1e+11      0.000   -2.92e-16   -2.92e-16
x10        -1.028e-16   1.78e-27  -5.76e+10      0.000   -1.03e-16   -1.03e-16
x11        -4.338e-16   4.31e-27  -1.01e+11      0.000   -4.34e-16   -4.34e-16
x12          1.72e-16   4.33e-27   3.97e+10      0.000    1.72e-16    1.72e-16
x13        -3.011e-16   4.36e-27  -6.91e+10      0.000   -3.01e-16   -3.01e-16
x14        -2.611e-16   1.27e-26  -2.06e+10      0.000   -2.61e-16   -2.61e-16
x15          1.53e-14   4.46e-27   3.43e+12      0.000    1.53e-14    1.53e-14
x16        -1.401e-14   5.45e-27  -2.57e+12      0.000    -1.4e-14    -1.4e-14
x17         2.316e-14   4.12e-27   5.62e+12      0.000    2.32e-14    2.32e-14
x18        -3.727e-15   3.71e-27  -1.01e+12      0.000   -3.73e-15   -3.73e-15
x19        -1.361e-14   4.94e-27  -2.75e+12      0.000   -1.36e-14   -1.36e-14
x20        -5.277e-15   6.08e-27  -8.68e+11      0.000   -5.28e-15   -5.28e-15
x21         1.178e-18   3.12e-27   3.77e+08      0.000    1.18e-18    1.18e-18
x22        -8.779e-17   1.74e-29  -5.05e+12      0.000   -8.78e-17   -8.78e-17
x23         3.183e-17   5.91e-29   5.39e+11      0.000    3.18e-17    3.18e-17
x24        -1.683e-16   1.41e-26  -1.19e+10      0.000   -1.68e-16   -1.68e-16
x25         8.988e-17   1.48e-30   6.08e+13      0.000    8.99e-17    8.99e-17
x26         4.435e-17   1.58e-26    2.8e+09      0.000    4.44e-17    4.44e-17
x27         1.538e-16   8.87e-27   1.73e+10      0.000    1.54e-16    1.54e-16
x28         1.635e-16   1.22e-26   1.34e+10      0.000    1.63e-16    1.63e-16
x29         1.474e-16   6.34e-27   2.33e+10      0.000    1.47e-16    1.47e-16
ar.L1         -0.9879   1.21e-22  -8.16e+21      0.000      -0.988      -0.988
ar.L2         -0.4879   1.29e-22  -3.79e+21      0.000      -0.488      -0.488
sigma2          1e-10   6.99e-11      1.432      0.152   -3.69e-11    2.37e-10
===================================================================================
Ljung-Box (L1) (Q):                  57.29   Jarque-Bera (JB):            559955.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.13   Skew:                             0.64
Prob(H) (two-sided):                  0.00   Kurtosis:                       132.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/mlemodel.py:2968: RuntimeWarning: divide by zero encountered in true_divide
  return self.params / self.bse
ARIMA order: (2, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.45223, saving model to LSTM7.h5
45/45 - 3s - loss: 0.2336 - mse: 0.2336 - mae: 0.3737 - val_loss: 0.4522 - val_mse: 0.4522 - val_mae: 0.6470 - lr: 0.0010 - 3s/epoch - 62ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.45223 to 0.17852, saving model to LSTM7.h5
45/45 - 0s - loss: 0.0456 - mse: 0.0456 - mae: 0.1706 - val_loss: 0.1785 - val_mse: 0.1785 - val_mae: 0.3874 - lr: 0.0010 - 226ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.17852 to 0.11256, saving model to LSTM7.h5
45/45 - 0s - loss: 0.0233 - mse: 0.0233 - mae: 0.1223 - val_loss: 0.1126 - val_mse: 0.1126 - val_mae: 0.2916 - lr: 0.0010 - 275ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss improved from 0.11256 to 0.10893, saving model to LSTM7.h5
45/45 - 0s - loss: 0.0170 - mse: 0.0170 - mae: 0.1034 - val_loss: 0.1089 - val_mse: 0.1089 - val_mae: 0.2845 - lr: 0.0010 - 216ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0126 - mse: 0.0126 - mae: 0.0898 - val_loss: 0.1238 - val_mse: 0.1238 - val_mae: 0.3087 - lr: 0.0010 - 193ms/epoch - 4ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0102 - mse: 0.0102 - mae: 0.0806 - val_loss: 0.1288 - val_mse: 0.1288 - val_mae: 0.3160 - lr: 0.0010 - 205ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0119 - mse: 0.0119 - mae: 0.0866 - val_loss: 0.1292 - val_mse: 0.1292 - val_mae: 0.3164 - lr: 0.0010 - 274ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0106 - mse: 0.0106 - mae: 0.0807 - val_loss: 0.1173 - val_mse: 0.1173 - val_mae: 0.2971 - lr: 0.0010 - 203ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00009: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0097 - mse: 0.0097 - mae: 0.0784 - val_loss: 0.1345 - val_mse: 0.1345 - val_mae: 0.3226 - lr: 0.0010 - 184ms/epoch - 4ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0090 - mse: 0.0090 - mae: 0.0733 - val_loss: 0.1298 - val_mse: 0.1298 - val_mae: 0.3158 - lr: 1.0000e-04 - 198ms/epoch - 4ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0703 - val_loss: 0.1264 - val_mse: 0.1264 - val_mae: 0.3108 - lr: 1.0000e-04 - 205ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0090 - mse: 0.0090 - mae: 0.0736 - val_loss: 0.1259 - val_mse: 0.1259 - val_mae: 0.3097 - lr: 1.0000e-04 - 246ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0679 - val_loss: 0.1230 - val_mse: 0.1230 - val_mae: 0.3049 - lr: 1.0000e-04 - 265ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00014: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0082 - mse: 0.0082 - mae: 0.0716 - val_loss: 0.1208 - val_mse: 0.1208 - val_mae: 0.3013 - lr: 1.0000e-04 - 189ms/epoch - 4ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0084 - mse: 0.0084 - mae: 0.0717 - val_loss: 0.1208 - val_mse: 0.1208 - val_mae: 0.3012 - lr: 1.0000e-05 - 263ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0077 - mse: 0.0077 - mae: 0.0683 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3009 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0689 - val_loss: 0.1204 - val_mse: 0.1204 - val_mae: 0.3005 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0660 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3010 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 19/500

Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00019: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0694 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3009 - lr: 1.0000e-05 - 238ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0670 - val_loss: 0.1208 - val_mse: 0.1208 - val_mae: 0.3012 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0656 - val_loss: 0.1205 - val_mse: 0.1205 - val_mae: 0.3007 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0671 - val_loss: 0.1207 - val_mse: 0.1207 - val_mae: 0.3009 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0670 - val_loss: 0.1207 - val_mse: 0.1207 - val_mae: 0.3009 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0079 - mse: 0.0079 - mae: 0.0696 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3008 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0686 - val_loss: 0.1207 - val_mse: 0.1207 - val_mae: 0.3009 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0668 - val_loss: 0.1204 - val_mse: 0.1204 - val_mae: 0.3005 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0682 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3007 - lr: 1.0000e-05 - 195ms/epoch - 4ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0685 - val_loss: 0.1205 - val_mse: 0.1205 - val_mae: 0.3006 - lr: 1.0000e-05 - 210ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0672 - val_loss: 0.1204 - val_mse: 0.1204 - val_mae: 0.3003 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0666 - val_loss: 0.1204 - val_mse: 0.1204 - val_mae: 0.3003 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0659 - val_loss: 0.1205 - val_mse: 0.1205 - val_mae: 0.3003 - lr: 1.0000e-05 - 202ms/epoch - 4ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0675 - val_loss: 0.1208 - val_mse: 0.1208 - val_mae: 0.3008 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0665 - val_loss: 0.1211 - val_mse: 0.1211 - val_mae: 0.3013 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0665 - val_loss: 0.1213 - val_mse: 0.1213 - val_mae: 0.3016 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0686 - val_loss: 0.1213 - val_mse: 0.1213 - val_mae: 0.3016 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0080 - mse: 0.0080 - mae: 0.0689 - val_loss: 0.1211 - val_mse: 0.1211 - val_mae: 0.3013 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0644 - val_loss: 0.1210 - val_mse: 0.1210 - val_mae: 0.3010 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0680 - val_loss: 0.1209 - val_mse: 0.1209 - val_mae: 0.3009 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0646 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3003 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0693 - val_loss: 0.1205 - val_mse: 0.1205 - val_mae: 0.3003 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0070 - mse: 0.0070 - mae: 0.0672 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3004 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0649 - val_loss: 0.1207 - val_mse: 0.1207 - val_mae: 0.3005 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0672 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3003 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0678 - val_loss: 0.1206 - val_mse: 0.1206 - val_mae: 0.3003 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0648 - val_loss: 0.1209 - val_mse: 0.1209 - val_mae: 0.3008 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0665 - val_loss: 0.1209 - val_mse: 0.1209 - val_mae: 0.3006 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0657 - val_loss: 0.1213 - val_mse: 0.1213 - val_mae: 0.3012 - lr: 1.0000e-05 - 244ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0609 - val_loss: 0.1219 - val_mse: 0.1219 - val_mae: 0.3022 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0072 - mse: 0.0072 - mae: 0.0660 - val_loss: 0.1219 - val_mse: 0.1219 - val_mae: 0.3022 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0656 - val_loss: 0.1218 - val_mse: 0.1218 - val_mae: 0.3020 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0673 - val_loss: 0.1221 - val_mse: 0.1221 - val_mae: 0.3025 - lr: 1.0000e-05 - 247ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0066 - mse: 0.0066 - mae: 0.0633 - val_loss: 0.1218 - val_mse: 0.1218 - val_mae: 0.3020 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0623 - val_loss: 0.1214 - val_mse: 0.1214 - val_mae: 0.3013 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.10893
45/45 - 0s - loss: 0.0071 - mse: 0.0071 - mae: 0.0657 - val_loss: 0.1210 - val_mse: 0.1210 - val_mae: 0.3007 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 00054: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 42.30488040231578 
RMSE:	 6.504220199402522 
MAPE:	 5.010195929360332

DEMA
Prediction vs Close:		55.6% Accuracy
Prediction vs Prediction:	54.48% Accuracy
MSE:	 23.305922116020078 
RMSE:	 4.827620751055335 
MAPE:	 3.7452201197397774

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 18.082341646298453 
RMSE:	 4.252333670621163 
MAPE:	 3.4333194517527637
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.238, Time=3.57 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.578, Time=5.49 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16746.296, Time=8.42 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.578, Time=8.49 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16987.591, Time=3.66 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16395.520, Time=12.87 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17063.555, Time=12.59 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.578, Time=10.44 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16082.554, Time=20.02 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-15249.608, Time=19.18 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 104.740 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8563.778
Date:                Sun, 12 Dec 2021   AIC                         -17063.555
Time:                        18:49:53   BIC                         -16913.448
Sample:                             0   HQIC                        -17005.908
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.495e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x2         -1.485e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x3         -1.518e-10      0.000  -1.21e-06      1.000      -0.000       0.000
x4             1.0000      0.000   8075.329      0.000       1.000       1.000
x5         -1.356e-10      0.000  -1.15e-06      1.000      -0.000       0.000
x6         -2.861e-09      0.000  -2.38e-05      1.000      -0.000       0.000
x7         -1.374e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x8         -1.371e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x9         -7.133e-11    7.1e-06  -1.01e-05      1.000   -1.39e-05    1.39e-05
x10         -1.23e-10   4.21e-05  -2.92e-06      1.000   -8.24e-05    8.24e-05
x11        -1.357e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x12        -1.401e-10      0.000  -1.11e-06      1.000      -0.000       0.000
x13        -1.436e-10      0.000  -1.16e-06      1.000      -0.000       0.000
x14        -1.179e-09      0.000  -3.22e-06      1.000      -0.001       0.001
x15        -1.651e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x16        -1.064e-10      0.000  -9.62e-07      1.000      -0.000       0.000
x17        -1.041e-10      0.000  -9.53e-07      1.000      -0.000       0.000
x18        -4.477e-10      0.000  -1.99e-06      1.000      -0.000       0.000
x19        -1.816e-10      0.000  -1.26e-06      1.000      -0.000       0.000
x20         -4.37e-10      0.000  -1.96e-06      1.000      -0.000       0.000
x21        -1.371e-09    9.1e-05  -1.51e-05      1.000      -0.000       0.000
x22        -1.059e-11        nan        nan        nan         nan         nan
x23        -9.902e-11   3.83e-09     -0.026      0.979   -7.61e-09    7.41e-09
x24        -5.521e-09      0.000  -1.34e-05      1.000      -0.001       0.001
x25        -4.621e-09   6.42e-05   -7.2e-05      1.000      -0.000       0.000
x26        -1.587e-09      0.000  -3.73e-06      1.000      -0.001       0.001
x27        -8.504e-10      0.000  -2.79e-06      1.000      -0.001       0.001
x28        -1.122e-09      0.000  -3.14e-06      1.000      -0.001       0.001
x29        -6.091e-10      0.000  -2.45e-06      1.000      -0.000       0.000
ma.L1         -1.3318   7.32e-07  -1.82e+06      0.000      -1.332      -1.332
ma.L2          0.3767   7.56e-07   4.98e+05      0.000       0.377       0.377
sigma2      9.093e-11   6.97e-11      1.304      0.192   -4.57e-11    2.28e-10
===================================================================================
Ljung-Box (L1) (Q):                  76.00   Jarque-Bera (JB):            304933.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.03   Skew:                             1.65
Prob(H) (two-sided):                  0.00   Kurtosis:                        98.29
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.19e+28. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.51896, saving model to LSTM7.h5
58/58 - 2s - loss: 0.1462 - mse: 0.1462 - mae: 0.2950 - val_loss: 0.5190 - val_mse: 0.5190 - val_mae: 0.6940 - lr: 0.0010 - 2s/epoch - 40ms/step
Epoch 2/500

Epoch 00002: val_loss improved from 0.51896 to 0.03828, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0396 - mse: 0.0396 - mae: 0.1542 - val_loss: 0.0383 - val_mse: 0.0383 - val_mae: 0.1708 - lr: 0.0010 - 292ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.03828 to 0.02764, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0300 - mse: 0.0300 - mae: 0.1367 - val_loss: 0.0276 - val_mse: 0.0276 - val_mae: 0.1316 - lr: 0.0010 - 295ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.02764
58/58 - 0s - loss: 0.0183 - mse: 0.0183 - mae: 0.1075 - val_loss: 0.0306 - val_mse: 0.0306 - val_mae: 0.1346 - lr: 0.0010 - 286ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.02764 to 0.02589, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0167 - mse: 0.0167 - mae: 0.1022 - val_loss: 0.0259 - val_mse: 0.0259 - val_mae: 0.1259 - lr: 0.0010 - 322ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: val_loss improved from 0.02589 to 0.02461, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0887 - val_loss: 0.0246 - val_mse: 0.0246 - val_mae: 0.1241 - lr: 0.0010 - 277ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.02461
58/58 - 0s - loss: 0.0112 - mse: 0.0112 - mae: 0.0834 - val_loss: 0.0266 - val_mse: 0.0266 - val_mae: 0.1266 - lr: 0.0010 - 267ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss improved from 0.02461 to 0.02069, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0087 - mse: 0.0087 - mae: 0.0732 - val_loss: 0.0207 - val_mse: 0.0207 - val_mae: 0.1155 - lr: 0.0010 - 280ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss improved from 0.02069 to 0.01874, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0092 - mse: 0.0092 - mae: 0.0751 - val_loss: 0.0187 - val_mse: 0.0187 - val_mae: 0.1096 - lr: 0.0010 - 259ms/epoch - 4ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.01874
58/58 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0713 - val_loss: 0.0188 - val_mse: 0.0188 - val_mae: 0.1103 - lr: 0.0010 - 281ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: val_loss improved from 0.01874 to 0.01718, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0652 - val_loss: 0.0172 - val_mse: 0.0172 - val_mae: 0.1075 - lr: 0.0010 - 248ms/epoch - 4ms/step
Epoch 12/500

Epoch 00012: val_loss improved from 0.01718 to 0.01694, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0073 - mse: 0.0073 - mae: 0.0669 - val_loss: 0.0169 - val_mse: 0.0169 - val_mae: 0.1040 - lr: 0.0010 - 271ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss improved from 0.01694 to 0.01646, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0587 - val_loss: 0.0165 - val_mse: 0.0165 - val_mae: 0.1074 - lr: 0.0010 - 294ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.01646
58/58 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0638 - val_loss: 0.0169 - val_mse: 0.0169 - val_mae: 0.1048 - lr: 0.0010 - 255ms/epoch - 4ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.01646
58/58 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0615 - val_loss: 0.0177 - val_mse: 0.0177 - val_mae: 0.1121 - lr: 0.0010 - 253ms/epoch - 4ms/step
Epoch 16/500

Epoch 00016: val_loss improved from 0.01646 to 0.01602, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0075 - mse: 0.0075 - mae: 0.0678 - val_loss: 0.0160 - val_mse: 0.0160 - val_mae: 0.1003 - lr: 0.0010 - 307ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.01602
58/58 - 0s - loss: 0.0065 - mse: 0.0065 - mae: 0.0613 - val_loss: 0.0184 - val_mse: 0.0184 - val_mae: 0.1138 - lr: 0.0010 - 252ms/epoch - 4ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.01602
58/58 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0654 - val_loss: 0.0188 - val_mse: 0.0188 - val_mae: 0.1090 - lr: 0.0010 - 264ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.01602
58/58 - 0s - loss: 0.0081 - mse: 0.0081 - mae: 0.0704 - val_loss: 0.0231 - val_mse: 0.0231 - val_mae: 0.1272 - lr: 0.0010 - 260ms/epoch - 4ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.01602
58/58 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0665 - val_loss: 0.0172 - val_mse: 0.0172 - val_mae: 0.1047 - lr: 0.0010 - 264ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00021: val_loss did not improve from 0.01602
58/58 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0611 - val_loss: 0.0216 - val_mse: 0.0216 - val_mae: 0.1211 - lr: 0.0010 - 273ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss improved from 0.01602 to 0.01467, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0181 - mse: 0.0181 - mae: 0.1165 - val_loss: 0.0147 - val_mse: 0.0147 - val_mae: 0.0997 - lr: 1.0000e-04 - 250ms/epoch - 4ms/step
Epoch 23/500

Epoch 00023: val_loss improved from 0.01467 to 0.01324, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0067 - mse: 0.0067 - mae: 0.0651 - val_loss: 0.0132 - val_mse: 0.0132 - val_mae: 0.0947 - lr: 1.0000e-04 - 285ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss improved from 0.01324 to 0.01202, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0562 - val_loss: 0.0120 - val_mse: 0.0120 - val_mae: 0.0900 - lr: 1.0000e-04 - 269ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss improved from 0.01202 to 0.01156, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0563 - val_loss: 0.0116 - val_mse: 0.0116 - val_mae: 0.0880 - lr: 1.0000e-04 - 265ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss improved from 0.01156 to 0.01131, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0534 - val_loss: 0.0113 - val_mse: 0.0113 - val_mae: 0.0867 - lr: 1.0000e-04 - 242ms/epoch - 4ms/step
Epoch 27/500

Epoch 00027: val_loss improved from 0.01131 to 0.01109, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0528 - val_loss: 0.0111 - val_mse: 0.0111 - val_mae: 0.0855 - lr: 1.0000e-04 - 278ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.01109 to 0.01092, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0530 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0846 - lr: 1.0000e-04 - 293ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss improved from 0.01092 to 0.01081, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0511 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0838 - lr: 1.0000e-04 - 294ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss improved from 0.01081 to 0.01073, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0510 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0834 - lr: 1.0000e-04 - 268ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss improved from 0.01073 to 0.01065, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0503 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0827 - lr: 1.0000e-04 - 297ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss improved from 0.01065 to 0.01059, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0823 - lr: 1.0000e-04 - 265ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.01059
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0497 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0822 - lr: 1.0000e-04 - 265ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss improved from 0.01059 to 0.01048, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0480 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0815 - lr: 1.0000e-04 - 279ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss improved from 0.01048 to 0.01041, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0504 - val_loss: 0.0104 - val_mse: 0.0104 - val_mae: 0.0813 - lr: 1.0000e-04 - 253ms/epoch - 4ms/step
Epoch 36/500

Epoch 00036: val_loss improved from 0.01041 to 0.01031, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0494 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0812 - lr: 1.0000e-04 - 262ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss improved from 0.01031 to 0.01023, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0511 - val_loss: 0.0102 - val_mse: 0.0102 - val_mae: 0.0809 - lr: 1.0000e-04 - 256ms/epoch - 4ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.01023
58/58 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0489 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0805 - lr: 1.0000e-04 - 286ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss improved from 0.01023 to 0.01012, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0478 - val_loss: 0.0101 - val_mse: 0.0101 - val_mae: 0.0803 - lr: 1.0000e-04 - 283ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss improved from 0.01012 to 0.01011, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0482 - val_loss: 0.0101 - val_mse: 0.0101 - val_mae: 0.0800 - lr: 1.0000e-04 - 288ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss improved from 0.01011 to 0.01007, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0476 - val_loss: 0.0101 - val_mse: 0.0101 - val_mae: 0.0800 - lr: 1.0000e-04 - 306ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss improved from 0.01007 to 0.01004, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0495 - val_loss: 0.0100 - val_mse: 0.0100 - val_mae: 0.0800 - lr: 1.0000e-04 - 313ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss improved from 0.01004 to 0.00994, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0475 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0796 - lr: 1.0000e-04 - 269ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss improved from 0.00994 to 0.00990, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0473 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0795 - lr: 1.0000e-04 - 311ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.00990
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0487 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0792 - lr: 1.0000e-04 - 251ms/epoch - 4ms/step
Epoch 46/500

Epoch 00046: val_loss improved from 0.00990 to 0.00986, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0466 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0789 - lr: 1.0000e-04 - 281ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss improved from 0.00986 to 0.00970, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0461 - val_loss: 0.0097 - val_mse: 0.0097 - val_mae: 0.0786 - lr: 1.0000e-04 - 253ms/epoch - 4ms/step
Epoch 48/500

Epoch 00048: val_loss improved from 0.00970 to 0.00965, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0470 - val_loss: 0.0096 - val_mse: 0.0096 - val_mae: 0.0785 - lr: 1.0000e-04 - 324ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss improved from 0.00965 to 0.00950, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0462 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0781 - lr: 1.0000e-04 - 250ms/epoch - 4ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.00950
58/58 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0471 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-04 - 270ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss improved from 0.00950 to 0.00950, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0455 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0780 - lr: 1.0000e-04 - 283ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.00950
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0465 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0784 - lr: 1.0000e-04 - 282ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss improved from 0.00950 to 0.00947, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0450 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0777 - lr: 1.0000e-04 - 247ms/epoch - 4ms/step
Epoch 54/500

Epoch 00054: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00054: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0460 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0774 - lr: 1.0000e-04 - 237ms/epoch - 4ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0458 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0775 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0428 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0776 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0457 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0777 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0456 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0777 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 59/500

Epoch 00059: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00059: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0454 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0777 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 60/500

Epoch 00060: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0453 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0777 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 61/500

Epoch 00061: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0434 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0777 - lr: 1.0000e-05 - 261ms/epoch - 4ms/step
Epoch 62/500

Epoch 00062: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0441 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 63/500

Epoch 00063: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 301ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0460 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0448 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0449 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0445 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0445 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 234ms/epoch - 4ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0464 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0443 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.00947
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0443 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 72/500

Epoch 00072: val_loss improved from 0.00947 to 0.00946, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0433 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 73/500

Epoch 00073: val_loss improved from 0.00946 to 0.00945, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0436 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0454 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0450 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0780 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 76/500

Epoch 00076: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0454 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0780 - lr: 1.0000e-05 - 229ms/epoch - 4ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0425 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0457 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0780 - lr: 1.0000e-05 - 313ms/epoch - 5ms/step
Epoch 79/500

Epoch 00079: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0431 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0783 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 80/500

Epoch 00080: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0446 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0780 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 81/500

Epoch 00081: val_loss improved from 0.00945 to 0.00945, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0437 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0778 - lr: 1.0000e-05 - 281ms/epoch - 5ms/step
Epoch 82/500

Epoch 00082: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0455 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0778 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 83/500

Epoch 00083: val_loss did not improve from 0.00945
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0447 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0779 - lr: 1.0000e-05 - 235ms/epoch - 4ms/step
Epoch 84/500

Epoch 00084: val_loss improved from 0.00945 to 0.00944, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0456 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0777 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 85/500

Epoch 00085: val_loss improved from 0.00944 to 0.00944, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0433 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0778 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 86/500

Epoch 00086: val_loss did not improve from 0.00944
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0430 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0781 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 87/500

Epoch 00087: val_loss did not improve from 0.00944
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0447 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0780 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 88/500

Epoch 00088: val_loss did not improve from 0.00944
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0455 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0780 - lr: 1.0000e-05 - 237ms/epoch - 4ms/step
Epoch 89/500

Epoch 00089: val_loss did not improve from 0.00944
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0442 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0781 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 90/500

Epoch 00090: val_loss did not improve from 0.00944
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0427 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0780 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 91/500

Epoch 00091: val_loss did not improve from 0.00944
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0445 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0780 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 92/500

Epoch 00092: val_loss improved from 0.00944 to 0.00942, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0438 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0779 - lr: 1.0000e-05 - 305ms/epoch - 5ms/step
Epoch 93/500

Epoch 00093: val_loss improved from 0.00942 to 0.00940, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0446 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0778 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 94/500

Epoch 00094: val_loss did not improve from 0.00940
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0459 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0778 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 95/500

Epoch 00095: val_loss improved from 0.00940 to 0.00939, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0453 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0777 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 96/500

Epoch 00096: val_loss improved from 0.00939 to 0.00938, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0451 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0777 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 97/500

Epoch 00097: val_loss improved from 0.00938 to 0.00937, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0434 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0777 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 98/500

Epoch 00098: val_loss did not improve from 0.00937
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0443 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0777 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 99/500

Epoch 00099: val_loss improved from 0.00937 to 0.00936, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0428 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0776 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 100/500

Epoch 00100: val_loss did not improve from 0.00936
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0436 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0777 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 101/500

Epoch 00101: val_loss improved from 0.00936 to 0.00933, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0416 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0775 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 102/500

Epoch 00102: val_loss improved from 0.00933 to 0.00933, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0414 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0774 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 103/500

Epoch 00103: val_loss did not improve from 0.00933
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0442 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0774 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 104/500

Epoch 00104: val_loss improved from 0.00933 to 0.00932, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0425 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0774 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 105/500

Epoch 00105: val_loss improved from 0.00932 to 0.00930, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0422 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0772 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 106/500

Epoch 00106: val_loss did not improve from 0.00930
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0772 - lr: 1.0000e-05 - 245ms/epoch - 4ms/step
Epoch 107/500

Epoch 00107: val_loss improved from 0.00930 to 0.00929, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0437 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0772 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 108/500

Epoch 00108: val_loss improved from 0.00929 to 0.00928, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0453 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0772 - lr: 1.0000e-05 - 335ms/epoch - 6ms/step
Epoch 109/500

Epoch 00109: val_loss improved from 0.00928 to 0.00926, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0456 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0771 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 110/500

Epoch 00110: val_loss improved from 0.00926 to 0.00925, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0423 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0771 - lr: 1.0000e-05 - 260ms/epoch - 4ms/step
Epoch 111/500

Epoch 00111: val_loss improved from 0.00925 to 0.00925, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0430 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0771 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 112/500

Epoch 00112: val_loss improved from 0.00925 to 0.00924, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0443 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0770 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 113/500

Epoch 00113: val_loss improved from 0.00924 to 0.00924, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0433 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0770 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 114/500

Epoch 00114: val_loss improved from 0.00924 to 0.00923, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 115/500

Epoch 00115: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0455 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0769 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 116/500

Epoch 00116: val_loss improved from 0.00923 to 0.00923, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0450 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0769 - lr: 1.0000e-05 - 276ms/epoch - 5ms/step
Epoch 117/500

Epoch 00117: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0425 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0769 - lr: 1.0000e-05 - 283ms/epoch - 5ms/step
Epoch 118/500

Epoch 00118: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0437 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0770 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 119/500

Epoch 00119: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0435 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0770 - lr: 1.0000e-05 - 260ms/epoch - 4ms/step
Epoch 120/500

Epoch 00120: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0441 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0770 - lr: 1.0000e-05 - 227ms/epoch - 4ms/step
Epoch 121/500

Epoch 00121: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0452 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0769 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 122/500

Epoch 00122: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0437 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0769 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 123/500

Epoch 00123: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0435 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0769 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 124/500

Epoch 00124: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0422 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 125/500

Epoch 00125: val_loss did not improve from 0.00923
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0428 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0769 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 126/500

Epoch 00126: val_loss improved from 0.00923 to 0.00922, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0424 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0769 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 127/500

Epoch 00127: val_loss improved from 0.00922 to 0.00921, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0439 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 273ms/epoch - 5ms/step
Epoch 128/500

Epoch 00128: val_loss did not improve from 0.00921
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0417 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 244ms/epoch - 4ms/step
Epoch 129/500

Epoch 00129: val_loss improved from 0.00921 to 0.00919, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0421 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 299ms/epoch - 5ms/step
Epoch 130/500

Epoch 00130: val_loss improved from 0.00919 to 0.00918, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0440 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 276ms/epoch - 5ms/step
Epoch 131/500

Epoch 00131: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0410 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 132/500

Epoch 00132: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0439 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 133/500

Epoch 00133: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0440 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 134/500

Epoch 00134: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0431 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 135/500

Epoch 00135: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0430 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 136/500

Epoch 00136: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0438 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 294ms/epoch - 5ms/step
Epoch 137/500

Epoch 00137: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0436 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0769 - lr: 1.0000e-05 - 294ms/epoch - 5ms/step
Epoch 138/500

Epoch 00138: val_loss did not improve from 0.00918
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0419 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 139/500

Epoch 00139: val_loss improved from 0.00918 to 0.00917, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0434 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0766 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 140/500

Epoch 00140: val_loss improved from 0.00917 to 0.00916, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0421 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 273ms/epoch - 5ms/step
Epoch 141/500

Epoch 00141: val_loss improved from 0.00916 to 0.00916, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0448 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0767 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 142/500

Epoch 00142: val_loss did not improve from 0.00916
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0449 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0768 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 143/500

Epoch 00143: val_loss improved from 0.00916 to 0.00915, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0435 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0766 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 144/500

Epoch 00144: val_loss improved from 0.00915 to 0.00913, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0431 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0765 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 145/500

Epoch 00145: val_loss improved from 0.00913 to 0.00912, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0764 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 146/500

Epoch 00146: val_loss improved from 0.00912 to 0.00911, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0407 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0763 - lr: 1.0000e-05 - 334ms/epoch - 6ms/step
Epoch 147/500

Epoch 00147: val_loss did not improve from 0.00911
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0419 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0763 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 148/500

Epoch 00148: val_loss did not improve from 0.00911
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0426 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0764 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 149/500

Epoch 00149: val_loss did not improve from 0.00911
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0417 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0764 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 150/500

Epoch 00150: val_loss improved from 0.00911 to 0.00909, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0424 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0762 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 151/500

Epoch 00151: val_loss improved from 0.00909 to 0.00909, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0435 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0761 - lr: 1.0000e-05 - 281ms/epoch - 5ms/step
Epoch 152/500

Epoch 00152: val_loss improved from 0.00909 to 0.00908, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0442 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0761 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 153/500

Epoch 00153: val_loss did not improve from 0.00908
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0400 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0763 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 154/500

Epoch 00154: val_loss improved from 0.00908 to 0.00907, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0437 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0762 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 155/500

Epoch 00155: val_loss improved from 0.00907 to 0.00906, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0432 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0760 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 156/500

Epoch 00156: val_loss improved from 0.00906 to 0.00905, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0419 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0759 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 157/500

Epoch 00157: val_loss did not improve from 0.00905
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0440 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0759 - lr: 1.0000e-05 - 311ms/epoch - 5ms/step
Epoch 158/500

Epoch 00158: val_loss did not improve from 0.00905
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0434 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0758 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 159/500

Epoch 00159: val_loss did not improve from 0.00905
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0438 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0759 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 160/500

Epoch 00160: val_loss improved from 0.00905 to 0.00904, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0426 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0757 - lr: 1.0000e-05 - 283ms/epoch - 5ms/step
Epoch 161/500

Epoch 00161: val_loss did not improve from 0.00904
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0415 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0757 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 162/500

Epoch 00162: val_loss improved from 0.00904 to 0.00902, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0413 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0757 - lr: 1.0000e-05 - 315ms/epoch - 5ms/step
Epoch 163/500

Epoch 00163: val_loss did not improve from 0.00902
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0426 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0758 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 164/500

Epoch 00164: val_loss did not improve from 0.00902
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0429 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0759 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 165/500

Epoch 00165: val_loss improved from 0.00902 to 0.00902, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0443 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0758 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 166/500

Epoch 00166: val_loss improved from 0.00902 to 0.00899, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0434 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0757 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 167/500

Epoch 00167: val_loss improved from 0.00899 to 0.00898, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0433 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0756 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 168/500

Epoch 00168: val_loss did not improve from 0.00898
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0433 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0756 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 169/500

Epoch 00169: val_loss did not improve from 0.00898
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0433 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0756 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 170/500

Epoch 00170: val_loss improved from 0.00898 to 0.00895, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0417 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0754 - lr: 1.0000e-05 - 284ms/epoch - 5ms/step
Epoch 171/500

Epoch 00171: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0439 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0754 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 172/500

Epoch 00172: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0755 - lr: 1.0000e-05 - 260ms/epoch - 4ms/step
Epoch 173/500

Epoch 00173: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0441 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0757 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 174/500

Epoch 00174: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0422 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0758 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 175/500

Epoch 00175: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0424 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0757 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 176/500

Epoch 00176: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0434 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0756 - lr: 1.0000e-05 - 257ms/epoch - 4ms/step
Epoch 177/500

Epoch 00177: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0414 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0756 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 178/500

Epoch 00178: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0440 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0756 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 179/500

Epoch 00179: val_loss did not improve from 0.00895
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0436 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0755 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 180/500

Epoch 00180: val_loss improved from 0.00895 to 0.00893, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0402 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0754 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 181/500

Epoch 00181: val_loss did not improve from 0.00893
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0426 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0754 - lr: 1.0000e-05 - 232ms/epoch - 4ms/step
Epoch 182/500

Epoch 00182: val_loss did not improve from 0.00893
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0444 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0753 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 183/500

Epoch 00183: val_loss improved from 0.00893 to 0.00893, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0425 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0753 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 184/500

Epoch 00184: val_loss improved from 0.00893 to 0.00891, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0401 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0752 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 185/500

Epoch 00185: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0427 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0753 - lr: 1.0000e-05 - 236ms/epoch - 4ms/step
Epoch 186/500

Epoch 00186: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0421 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0753 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 187/500

Epoch 00187: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0427 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0754 - lr: 1.0000e-05 - 309ms/epoch - 5ms/step
Epoch 188/500

Epoch 00188: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0451 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0754 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 189/500

Epoch 00189: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0416 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0753 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 190/500

Epoch 00190: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0410 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0753 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 191/500

Epoch 00191: val_loss did not improve from 0.00891
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0436 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0753 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 192/500

Epoch 00192: val_loss improved from 0.00891 to 0.00891, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0437 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0752 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 193/500

Epoch 00193: val_loss improved from 0.00891 to 0.00889, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0427 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0751 - lr: 1.0000e-05 - 308ms/epoch - 5ms/step
Epoch 194/500

Epoch 00194: val_loss did not improve from 0.00889
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0427 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0752 - lr: 1.0000e-05 - 246ms/epoch - 4ms/step
Epoch 195/500

Epoch 00195: val_loss improved from 0.00889 to 0.00889, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0446 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0751 - lr: 1.0000e-05 - 284ms/epoch - 5ms/step
Epoch 196/500

Epoch 00196: val_loss improved from 0.00889 to 0.00889, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0751 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 197/500

Epoch 00197: val_loss improved from 0.00889 to 0.00886, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0440 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0750 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 198/500

Epoch 00198: val_loss did not improve from 0.00886
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0751 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 199/500

Epoch 00199: val_loss improved from 0.00886 to 0.00885, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0422 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0750 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 200/500

Epoch 00200: val_loss did not improve from 0.00885
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0441 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0751 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 201/500

Epoch 00201: val_loss did not improve from 0.00885
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0411 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0750 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 202/500

Epoch 00202: val_loss did not improve from 0.00885
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0427 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0750 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 203/500

Epoch 00203: val_loss did not improve from 0.00885
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0426 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0750 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 204/500

Epoch 00204: val_loss did not improve from 0.00885
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0418 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0750 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 205/500

Epoch 00205: val_loss improved from 0.00885 to 0.00884, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0426 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0749 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 206/500

Epoch 00206: val_loss improved from 0.00884 to 0.00881, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0748 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 207/500

Epoch 00207: val_loss did not improve from 0.00881
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0424 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0748 - lr: 1.0000e-05 - 235ms/epoch - 4ms/step
Epoch 208/500

Epoch 00208: val_loss improved from 0.00881 to 0.00881, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0432 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0747 - lr: 1.0000e-05 - 284ms/epoch - 5ms/step
Epoch 209/500

Epoch 00209: val_loss improved from 0.00881 to 0.00880, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0406 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0747 - lr: 1.0000e-05 - 300ms/epoch - 5ms/step
Epoch 210/500

Epoch 00210: val_loss improved from 0.00880 to 0.00876, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0419 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 211/500

Epoch 00211: val_loss did not improve from 0.00876
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0448 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 212/500

Epoch 00212: val_loss improved from 0.00876 to 0.00876, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0410 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 213/500

Epoch 00213: val_loss improved from 0.00876 to 0.00876, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 214/500

Epoch 00214: val_loss did not improve from 0.00876
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0415 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 215/500

Epoch 00215: val_loss did not improve from 0.00876
58/58 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0455 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0746 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 216/500

Epoch 00216: val_loss improved from 0.00876 to 0.00875, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0433 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 217/500

Epoch 00217: val_loss improved from 0.00875 to 0.00874, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0412 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0745 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 218/500

Epoch 00218: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0418 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 276ms/epoch - 5ms/step
Epoch 219/500

Epoch 00219: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0423 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0746 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 220/500

Epoch 00220: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0416 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0746 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 221/500

Epoch 00221: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0426 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0746 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 222/500

Epoch 00222: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0387 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0746 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 223/500

Epoch 00223: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0432 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0746 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 224/500

Epoch 00224: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0425 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0745 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 225/500

Epoch 00225: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0415 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0745 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 226/500

Epoch 00226: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0409 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0745 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 227/500

Epoch 00227: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0423 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0744 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 228/500

Epoch 00228: val_loss improved from 0.00874 to 0.00874, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0431 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0743 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 229/500

Epoch 00229: val_loss did not improve from 0.00874
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0410 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0743 - lr: 1.0000e-05 - 234ms/epoch - 4ms/step
Epoch 230/500

Epoch 00230: val_loss improved from 0.00874 to 0.00871, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0442 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0742 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 231/500

Epoch 00231: val_loss improved from 0.00871 to 0.00870, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0424 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0742 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 232/500

Epoch 00232: val_loss improved from 0.00870 to 0.00866, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0425 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0740 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 233/500

Epoch 00233: val_loss improved from 0.00866 to 0.00865, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0398 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0740 - lr: 1.0000e-05 - 295ms/epoch - 5ms/step
Epoch 234/500

Epoch 00234: val_loss did not improve from 0.00865
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0418 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0741 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 235/500

Epoch 00235: val_loss did not improve from 0.00865
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0418 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0742 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 236/500

Epoch 00236: val_loss did not improve from 0.00865
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0439 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0741 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 237/500

Epoch 00237: val_loss improved from 0.00865 to 0.00865, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0420 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0740 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 238/500

Epoch 00238: val_loss improved from 0.00865 to 0.00864, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0424 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 239/500

Epoch 00239: val_loss improved from 0.00864 to 0.00863, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0416 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 261ms/epoch - 4ms/step
Epoch 240/500

Epoch 00240: val_loss did not improve from 0.00863
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0420 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0739 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 241/500

Epoch 00241: val_loss improved from 0.00863 to 0.00861, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0409 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 242/500

Epoch 00242: val_loss did not improve from 0.00861
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0426 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0739 - lr: 1.0000e-05 - 236ms/epoch - 4ms/step
Epoch 243/500

Epoch 00243: val_loss did not improve from 0.00861
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0429 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0739 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 244/500

Epoch 00244: val_loss did not improve from 0.00861
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0420 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 271ms/epoch - 5ms/step
Epoch 245/500

Epoch 00245: val_loss did not improve from 0.00861
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0420 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 246/500

Epoch 00246: val_loss improved from 0.00861 to 0.00861, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0396 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 247/500

Epoch 00247: val_loss improved from 0.00861 to 0.00860, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0414 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 248/500

Epoch 00248: val_loss did not improve from 0.00860
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0394 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 249/500

Epoch 00249: val_loss did not improve from 0.00860
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0413 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 250/500

Epoch 00250: val_loss improved from 0.00860 to 0.00859, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0428 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 319ms/epoch - 5ms/step
Epoch 251/500

Epoch 00251: val_loss did not improve from 0.00859
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0405 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 252/500

Epoch 00252: val_loss did not improve from 0.00859
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0420 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 253/500

Epoch 00253: val_loss did not improve from 0.00859
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0409 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 254/500

Epoch 00254: val_loss did not improve from 0.00859
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0413 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0738 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 255/500

Epoch 00255: val_loss improved from 0.00859 to 0.00858, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0414 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 256/500

Epoch 00256: val_loss improved from 0.00858 to 0.00857, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0412 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 257/500

Epoch 00257: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0422 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 258/500

Epoch 00258: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0401 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 281ms/epoch - 5ms/step
Epoch 259/500

Epoch 00259: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0412 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 260/500

Epoch 00260: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0433 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 261/500

Epoch 00261: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0421 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0737 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 262/500

Epoch 00262: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0410 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 263/500

Epoch 00263: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0401 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 264/500

Epoch 00264: val_loss did not improve from 0.00857
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0404 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 265/500

Epoch 00265: val_loss improved from 0.00857 to 0.00854, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0400 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0734 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 266/500

Epoch 00266: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0414 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 267/500

Epoch 00267: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0396 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 268/500

Epoch 00268: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 269/500

Epoch 00269: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0417 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0735 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 270/500

Epoch 00270: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0416 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0735 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 271/500

Epoch 00271: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0735 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 272/500

Epoch 00272: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 273/500

Epoch 00273: val_loss did not improve from 0.00854
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0419 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0736 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 274/500

Epoch 00274: val_loss improved from 0.00854 to 0.00853, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0402 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0735 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 275/500

Epoch 00275: val_loss improved from 0.00853 to 0.00851, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0424 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0733 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 276/500

Epoch 00276: val_loss did not improve from 0.00851
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0421 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0733 - lr: 1.0000e-05 - 237ms/epoch - 4ms/step
Epoch 277/500

Epoch 00277: val_loss did not improve from 0.00851
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0420 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0734 - lr: 1.0000e-05 - 245ms/epoch - 4ms/step
Epoch 278/500

Epoch 00278: val_loss did not improve from 0.00851
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0422 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0734 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 279/500

Epoch 00279: val_loss did not improve from 0.00851
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0410 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0734 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 280/500

Epoch 00280: val_loss improved from 0.00851 to 0.00850, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0389 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0733 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 281/500

Epoch 00281: val_loss did not improve from 0.00850
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0732 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 282/500

Epoch 00282: val_loss did not improve from 0.00850
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0414 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0732 - lr: 1.0000e-05 - 299ms/epoch - 5ms/step
Epoch 283/500

Epoch 00283: val_loss did not improve from 0.00850
58/58 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0431 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0733 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 284/500

Epoch 00284: val_loss improved from 0.00850 to 0.00848, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0403 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 285/500

Epoch 00285: val_loss did not improve from 0.00848
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0420 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 236ms/epoch - 4ms/step
Epoch 286/500

Epoch 00286: val_loss improved from 0.00848 to 0.00846, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0404 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 294ms/epoch - 5ms/step
Epoch 287/500

Epoch 00287: val_loss improved from 0.00846 to 0.00846, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 288/500

Epoch 00288: val_loss did not improve from 0.00846
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0416 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 289/500

Epoch 00289: val_loss did not improve from 0.00846
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0405 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 264ms/epoch - 5ms/step
Epoch 290/500

Epoch 00290: val_loss did not improve from 0.00846
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0398 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 291/500

Epoch 00291: val_loss did not improve from 0.00846
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 292/500

Epoch 00292: val_loss did not improve from 0.00846
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0401 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 293/500

Epoch 00293: val_loss improved from 0.00846 to 0.00844, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0730 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 294/500

Epoch 00294: val_loss did not improve from 0.00844
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0413 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 295/500

Epoch 00295: val_loss did not improve from 0.00844
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0408 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0731 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 296/500

Epoch 00296: val_loss did not improve from 0.00844
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0413 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0730 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 297/500

Epoch 00297: val_loss did not improve from 0.00844
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0424 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0730 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 298/500

Epoch 00298: val_loss improved from 0.00844 to 0.00843, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0398 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0729 - lr: 1.0000e-05 - 261ms/epoch - 4ms/step
Epoch 299/500

Epoch 00299: val_loss improved from 0.00843 to 0.00842, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0422 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0729 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 300/500

Epoch 00300: val_loss improved from 0.00842 to 0.00840, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0430 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0728 - lr: 1.0000e-05 - 295ms/epoch - 5ms/step
Epoch 301/500

Epoch 00301: val_loss improved from 0.00840 to 0.00839, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0404 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0727 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 302/500

Epoch 00302: val_loss did not improve from 0.00839
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0402 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0727 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 303/500

Epoch 00303: val_loss improved from 0.00839 to 0.00838, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0395 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0726 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 304/500

Epoch 00304: val_loss improved from 0.00838 to 0.00838, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0412 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0727 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 305/500

Epoch 00305: val_loss did not improve from 0.00838
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0405 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0727 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 306/500

Epoch 00306: val_loss improved from 0.00838 to 0.00837, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0408 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0726 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 307/500

Epoch 00307: val_loss improved from 0.00837 to 0.00836, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0408 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0726 - lr: 1.0000e-05 - 315ms/epoch - 5ms/step
Epoch 308/500

Epoch 00308: val_loss improved from 0.00836 to 0.00834, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0409 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0724 - lr: 1.0000e-05 - 276ms/epoch - 5ms/step
Epoch 309/500

Epoch 00309: val_loss improved from 0.00834 to 0.00833, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0427 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0724 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 310/500

Epoch 00310: val_loss improved from 0.00833 to 0.00833, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0413 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0724 - lr: 1.0000e-05 - 254ms/epoch - 4ms/step
Epoch 311/500

Epoch 00311: val_loss improved from 0.00833 to 0.00833, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0417 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0724 - lr: 1.0000e-05 - 306ms/epoch - 5ms/step
Epoch 312/500

Epoch 00312: val_loss did not improve from 0.00833
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0409 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0725 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 313/500

Epoch 00313: val_loss improved from 0.00833 to 0.00831, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0428 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0723 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 314/500

Epoch 00314: val_loss did not improve from 0.00831
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0412 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0724 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 315/500

Epoch 00315: val_loss did not improve from 0.00831
58/58 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0434 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0723 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 316/500

Epoch 00316: val_loss improved from 0.00831 to 0.00830, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0422 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0723 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 317/500

Epoch 00317: val_loss did not improve from 0.00830
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0409 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0724 - lr: 1.0000e-05 - 229ms/epoch - 4ms/step
Epoch 318/500

Epoch 00318: val_loss improved from 0.00830 to 0.00829, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0414 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0722 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 319/500

Epoch 00319: val_loss improved from 0.00829 to 0.00828, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 309ms/epoch - 5ms/step
Epoch 320/500

Epoch 00320: val_loss improved from 0.00828 to 0.00827, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0410 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 321/500

Epoch 00321: val_loss did not improve from 0.00827
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0425 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 310ms/epoch - 5ms/step
Epoch 322/500

Epoch 00322: val_loss improved from 0.00827 to 0.00826, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0395 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 323/500

Epoch 00323: val_loss did not improve from 0.00826
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0423 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0723 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 324/500

Epoch 00324: val_loss did not improve from 0.00826
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0422 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 325/500

Epoch 00325: val_loss improved from 0.00826 to 0.00824, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0719 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 326/500

Epoch 00326: val_loss did not improve from 0.00824
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0420 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0719 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 327/500

Epoch 00327: val_loss improved from 0.00824 to 0.00822, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0395 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0718 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 328/500

Epoch 00328: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0417 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0720 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 329/500

Epoch 00329: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0409 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 330/500

Epoch 00330: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0720 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 331/500

Epoch 00331: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0414 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0721 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 332/500

Epoch 00332: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0402 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0720 - lr: 1.0000e-05 - 273ms/epoch - 5ms/step
Epoch 333/500

Epoch 00333: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0403 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0720 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 334/500

Epoch 00334: val_loss did not improve from 0.00822
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0402 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0720 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 335/500

Epoch 00335: val_loss improved from 0.00822 to 0.00821, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0414 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0718 - lr: 1.0000e-05 - 281ms/epoch - 5ms/step
Epoch 336/500

Epoch 00336: val_loss did not improve from 0.00821
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0423 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0719 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 337/500

Epoch 00337: val_loss did not improve from 0.00821
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0400 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0719 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 338/500

Epoch 00338: val_loss did not improve from 0.00821
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0399 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0719 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 339/500

Epoch 00339: val_loss improved from 0.00821 to 0.00821, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0407 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0718 - lr: 1.0000e-05 - 273ms/epoch - 5ms/step
Epoch 340/500

Epoch 00340: val_loss improved from 0.00821 to 0.00820, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0407 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0717 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 341/500

Epoch 00341: val_loss improved from 0.00820 to 0.00819, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0408 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0717 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 342/500

Epoch 00342: val_loss improved from 0.00819 to 0.00818, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0404 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 283ms/epoch - 5ms/step
Epoch 343/500

Epoch 00343: val_loss improved from 0.00818 to 0.00817, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0380 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 305ms/epoch - 5ms/step
Epoch 344/500

Epoch 00344: val_loss did not improve from 0.00817
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0388 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0718 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 345/500

Epoch 00345: val_loss did not improve from 0.00817
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0406 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0719 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 346/500

Epoch 00346: val_loss did not improve from 0.00817
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0387 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 347/500

Epoch 00347: val_loss improved from 0.00817 to 0.00815, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0416 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0715 - lr: 1.0000e-05 - 270ms/epoch - 5ms/step
Epoch 348/500

Epoch 00348: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0420 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 349/500

Epoch 00349: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0397 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 350/500

Epoch 00350: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0401 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0717 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 351/500

Epoch 00351: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0405 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 246ms/epoch - 4ms/step
Epoch 352/500

Epoch 00352: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0398 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 353/500

Epoch 00353: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0397 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0717 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 354/500

Epoch 00354: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0413 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0717 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 355/500

Epoch 00355: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0393 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 245ms/epoch - 4ms/step
Epoch 356/500

Epoch 00356: val_loss did not improve from 0.00815
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0401 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0715 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 357/500

Epoch 00357: val_loss improved from 0.00815 to 0.00815, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0399 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0714 - lr: 1.0000e-05 - 313ms/epoch - 5ms/step
Epoch 358/500

Epoch 00358: val_loss improved from 0.00815 to 0.00813, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0409 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0714 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 359/500

Epoch 00359: val_loss improved from 0.00813 to 0.00812, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0408 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0713 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 360/500

Epoch 00360: val_loss improved from 0.00812 to 0.00808, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0387 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0711 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 361/500

Epoch 00361: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0377 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0712 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 362/500

Epoch 00362: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0713 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 363/500

Epoch 00363: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0394 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0713 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 364/500

Epoch 00364: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0418 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0714 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 365/500

Epoch 00365: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0402 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0713 - lr: 1.0000e-05 - 319ms/epoch - 6ms/step
Epoch 366/500

Epoch 00366: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0394 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0714 - lr: 1.0000e-05 - 300ms/epoch - 5ms/step
Epoch 367/500

Epoch 00367: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0410 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0713 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 368/500

Epoch 00368: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0405 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0714 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 369/500

Epoch 00369: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0403 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0715 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 370/500

Epoch 00370: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0400 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0716 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 371/500

Epoch 00371: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0394 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0717 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 372/500

Epoch 00372: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0397 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0714 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 373/500

Epoch 00373: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0391 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0712 - lr: 1.0000e-05 - 256ms/epoch - 4ms/step
Epoch 374/500

Epoch 00374: val_loss did not improve from 0.00808
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0711 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 375/500

Epoch 00375: val_loss improved from 0.00808 to 0.00806, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0405 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0709 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 376/500

Epoch 00376: val_loss improved from 0.00806 to 0.00803, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0411 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0708 - lr: 1.0000e-05 - 261ms/epoch - 5ms/step
Epoch 377/500

Epoch 00377: val_loss improved from 0.00803 to 0.00802, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0408 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0708 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 378/500

Epoch 00378: val_loss did not improve from 0.00802
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0390 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0708 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 379/500

Epoch 00379: val_loss improved from 0.00802 to 0.00799, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0706 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 380/500

Epoch 00380: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0402 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0707 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 381/500

Epoch 00381: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0393 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0706 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 382/500

Epoch 00382: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0425 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0709 - lr: 1.0000e-05 - 244ms/epoch - 4ms/step
Epoch 383/500

Epoch 00383: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0416 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0709 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 384/500

Epoch 00384: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0399 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0711 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 385/500

Epoch 00385: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0413 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0710 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 386/500

Epoch 00386: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0401 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0711 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 387/500

Epoch 00387: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0378 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0710 - lr: 1.0000e-05 - 253ms/epoch - 4ms/step
Epoch 388/500

Epoch 00388: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0405 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0709 - lr: 1.0000e-05 - 244ms/epoch - 4ms/step
Epoch 389/500

Epoch 00389: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0383 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0707 - lr: 1.0000e-05 - 241ms/epoch - 4ms/step
Epoch 390/500

Epoch 00390: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0417 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0706 - lr: 1.0000e-05 - 256ms/epoch - 4ms/step
Epoch 391/500

Epoch 00391: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0424 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0708 - lr: 1.0000e-05 - 255ms/epoch - 4ms/step
Epoch 392/500

Epoch 00392: val_loss did not improve from 0.00799
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0416 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0706 - lr: 1.0000e-05 - 245ms/epoch - 4ms/step
Epoch 393/500

Epoch 00393: val_loss improved from 0.00799 to 0.00797, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0397 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0704 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 394/500

Epoch 00394: val_loss improved from 0.00797 to 0.00794, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0391 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 395/500

Epoch 00395: val_loss did not improve from 0.00794
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0385 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0703 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 396/500

Epoch 00396: val_loss improved from 0.00794 to 0.00792, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0385 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 397/500

Epoch 00397: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0425 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0703 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 398/500

Epoch 00398: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0400 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0704 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 399/500

Epoch 00399: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0404 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0704 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 400/500

Epoch 00400: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0390 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0704 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 401/500

Epoch 00401: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0401 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0704 - lr: 1.0000e-05 - 284ms/epoch - 5ms/step
Epoch 402/500

Epoch 00402: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0393 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0703 - lr: 1.0000e-05 - 257ms/epoch - 4ms/step
Epoch 403/500

Epoch 00403: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0412 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 255ms/epoch - 4ms/step
Epoch 404/500

Epoch 00404: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0386 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0703 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 405/500

Epoch 00405: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0415 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 306ms/epoch - 5ms/step
Epoch 406/500

Epoch 00406: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0415 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0703 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 407/500

Epoch 00407: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0391 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0703 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 408/500

Epoch 00408: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0400 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 409/500

Epoch 00409: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0411 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0704 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 410/500

Epoch 00410: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0390 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0705 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 411/500

Epoch 00411: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0403 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0705 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 412/500

Epoch 00412: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0387 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 291ms/epoch - 5ms/step
Epoch 413/500

Epoch 00413: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0402 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0702 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 414/500

Epoch 00414: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0703 - lr: 1.0000e-05 - 233ms/epoch - 4ms/step
Epoch 415/500

Epoch 00415: val_loss did not improve from 0.00792
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0400 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0701 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 416/500

Epoch 00416: val_loss improved from 0.00792 to 0.00787, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0382 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 295ms/epoch - 5ms/step
Epoch 417/500

Epoch 00417: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0393 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 256ms/epoch - 4ms/step
Epoch 418/500

Epoch 00418: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0023 - mse: 0.0023 - mae: 0.0375 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 241ms/epoch - 4ms/step
Epoch 419/500

Epoch 00419: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0390 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 420/500

Epoch 00420: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0381 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 245ms/epoch - 4ms/step
Epoch 421/500

Epoch 00421: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0395 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0701 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 422/500

Epoch 00422: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0402 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0701 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 423/500

Epoch 00423: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0407 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0701 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 424/500

Epoch 00424: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0403 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0701 - lr: 1.0000e-05 - 263ms/epoch - 5ms/step
Epoch 425/500

Epoch 00425: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0408 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 261ms/epoch - 5ms/step
Epoch 426/500

Epoch 00426: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0401 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 427/500

Epoch 00427: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0382 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0701 - lr: 1.0000e-05 - 273ms/epoch - 5ms/step
Epoch 428/500

Epoch 00428: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0386 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0702 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 429/500

Epoch 00429: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0412 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0702 - lr: 1.0000e-05 - 281ms/epoch - 5ms/step
Epoch 430/500

Epoch 00430: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0390 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 246ms/epoch - 4ms/step
Epoch 431/500

Epoch 00431: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0408 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0700 - lr: 1.0000e-05 - 234ms/epoch - 4ms/step
Epoch 432/500

Epoch 00432: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0391 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0698 - lr: 1.0000e-05 - 255ms/epoch - 4ms/step
Epoch 433/500

Epoch 00433: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0410 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0698 - lr: 1.0000e-05 - 315ms/epoch - 5ms/step
Epoch 434/500

Epoch 00434: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0386 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0699 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 435/500

Epoch 00435: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0383 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0699 - lr: 1.0000e-05 - 259ms/epoch - 4ms/step
Epoch 436/500

Epoch 00436: val_loss did not improve from 0.00787
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0393 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0698 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 437/500

Epoch 00437: val_loss improved from 0.00787 to 0.00785, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0393 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0696 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 438/500

Epoch 00438: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0414 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0696 - lr: 1.0000e-05 - 231ms/epoch - 4ms/step
Epoch 439/500

Epoch 00439: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0390 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0698 - lr: 1.0000e-05 - 232ms/epoch - 4ms/step
Epoch 440/500

Epoch 00440: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0393 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0699 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 441/500

Epoch 00441: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0400 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0696 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 442/500

Epoch 00442: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0392 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0697 - lr: 1.0000e-05 - 316ms/epoch - 5ms/step
Epoch 443/500

Epoch 00443: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0399 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0697 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 444/500

Epoch 00444: val_loss did not improve from 0.00785
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0398 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0696 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 445/500

Epoch 00445: val_loss improved from 0.00785 to 0.00784, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0388 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0695 - lr: 1.0000e-05 - 322ms/epoch - 6ms/step
Epoch 446/500

Epoch 00446: val_loss improved from 0.00784 to 0.00782, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0398 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0694 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 447/500

Epoch 00447: val_loss improved from 0.00782 to 0.00781, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0420 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 339ms/epoch - 6ms/step
Epoch 448/500

Epoch 00448: val_loss improved from 0.00781 to 0.00776, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 274ms/epoch - 5ms/step
Epoch 449/500

Epoch 00449: val_loss improved from 0.00776 to 0.00775, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0403 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0691 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 450/500

Epoch 00450: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0400 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 243ms/epoch - 4ms/step
Epoch 451/500

Epoch 00451: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0407 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 452/500

Epoch 00452: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0382 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 232ms/epoch - 4ms/step
Epoch 453/500

Epoch 00453: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0388 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 257ms/epoch - 4ms/step
Epoch 454/500

Epoch 00454: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0395 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 455/500

Epoch 00455: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0403 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 456/500

Epoch 00456: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0388 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 457/500

Epoch 00457: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0391 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 240ms/epoch - 4ms/step
Epoch 458/500

Epoch 00458: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0386 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 459/500

Epoch 00459: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0401 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 268ms/epoch - 5ms/step
Epoch 460/500

Epoch 00460: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0400 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 249ms/epoch - 4ms/step
Epoch 461/500

Epoch 00461: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0407 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 269ms/epoch - 5ms/step
Epoch 462/500

Epoch 00462: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0394 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 463/500

Epoch 00463: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0402 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0694 - lr: 1.0000e-05 - 293ms/epoch - 5ms/step
Epoch 464/500

Epoch 00464: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0395 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0693 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 465/500

Epoch 00465: val_loss did not improve from 0.00775
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0394 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 282ms/epoch - 5ms/step
Epoch 466/500

Epoch 00466: val_loss improved from 0.00775 to 0.00774, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0385 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0689 - lr: 1.0000e-05 - 265ms/epoch - 5ms/step
Epoch 467/500

Epoch 00467: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0400 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 250ms/epoch - 4ms/step
Epoch 468/500

Epoch 00468: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0409 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 248ms/epoch - 4ms/step
Epoch 469/500

Epoch 00469: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0401 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0692 - lr: 1.0000e-05 - 273ms/epoch - 5ms/step
Epoch 470/500

Epoch 00470: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0399 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 258ms/epoch - 4ms/step
Epoch 471/500

Epoch 00471: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0397 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 235ms/epoch - 4ms/step
Epoch 472/500

Epoch 00472: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0390 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0691 - lr: 1.0000e-05 - 235ms/epoch - 4ms/step
Epoch 473/500

Epoch 00473: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0382 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0689 - lr: 1.0000e-05 - 272ms/epoch - 5ms/step
Epoch 474/500

Epoch 00474: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0390 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 286ms/epoch - 5ms/step
Epoch 475/500

Epoch 00475: val_loss did not improve from 0.00774
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0393 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0689 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 476/500

Epoch 00476: val_loss improved from 0.00774 to 0.00773, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0394 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0687 - lr: 1.0000e-05 - 266ms/epoch - 5ms/step
Epoch 477/500

Epoch 00477: val_loss improved from 0.00773 to 0.00772, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0401 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0687 - lr: 1.0000e-05 - 334ms/epoch - 6ms/step
Epoch 478/500

Epoch 00478: val_loss improved from 0.00772 to 0.00769, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0388 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0686 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 479/500

Epoch 00479: val_loss improved from 0.00769 to 0.00769, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0377 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0686 - lr: 1.0000e-05 - 300ms/epoch - 5ms/step
Epoch 480/500

Epoch 00480: val_loss improved from 0.00769 to 0.00768, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0411 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0685 - lr: 1.0000e-05 - 318ms/epoch - 5ms/step
Epoch 481/500

Epoch 00481: val_loss improved from 0.00768 to 0.00767, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0385 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0685 - lr: 1.0000e-05 - 279ms/epoch - 5ms/step
Epoch 482/500

Epoch 00482: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0398 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0687 - lr: 1.0000e-05 - 244ms/epoch - 4ms/step
Epoch 483/500

Epoch 00483: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0410 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0686 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 484/500

Epoch 00484: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0406 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0689 - lr: 1.0000e-05 - 247ms/epoch - 4ms/step
Epoch 485/500

Epoch 00485: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0023 - mse: 0.0023 - mae: 0.0373 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0693 - lr: 1.0000e-05 - 252ms/epoch - 4ms/step
Epoch 486/500

Epoch 00486: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0384 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 244ms/epoch - 4ms/step
Epoch 487/500

Epoch 00487: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0386 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0688 - lr: 1.0000e-05 - 244ms/epoch - 4ms/step
Epoch 488/500

Epoch 00488: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0404 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0690 - lr: 1.0000e-05 - 313ms/epoch - 5ms/step
Epoch 489/500

Epoch 00489: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0407 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0686 - lr: 1.0000e-05 - 290ms/epoch - 5ms/step
Epoch 490/500

Epoch 00490: val_loss did not improve from 0.00767
58/58 - 0s - loss: 0.0028 - mse: 0.0028 - mae: 0.0404 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0686 - lr: 1.0000e-05 - 281ms/epoch - 5ms/step
Epoch 491/500

Epoch 00491: val_loss improved from 0.00767 to 0.00767, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0023 - mse: 0.0023 - mae: 0.0377 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0684 - lr: 1.0000e-05 - 275ms/epoch - 5ms/step
Epoch 492/500

Epoch 00492: val_loss improved from 0.00767 to 0.00763, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0395 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0683 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 493/500

Epoch 00493: val_loss improved from 0.00763 to 0.00761, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0396 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0683 - lr: 1.0000e-05 - 251ms/epoch - 4ms/step
Epoch 494/500

Epoch 00494: val_loss improved from 0.00761 to 0.00758, saving model to LSTM7.h5
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0393 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0682 - lr: 1.0000e-05 - 351ms/epoch - 6ms/step
Epoch 495/500

Epoch 00495: val_loss did not improve from 0.00758
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0388 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0681 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 496/500

Epoch 00496: val_loss did not improve from 0.00758
58/58 - 0s - loss: 0.0026 - mse: 0.0026 - mae: 0.0392 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0681 - lr: 1.0000e-05 - 238ms/epoch - 4ms/step
Epoch 497/500

Epoch 00497: val_loss did not improve from 0.00758
58/58 - 0s - loss: 0.0027 - mse: 0.0027 - mae: 0.0389 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0682 - lr: 1.0000e-05 - 267ms/epoch - 5ms/step
Epoch 498/500

Epoch 00498: val_loss did not improve from 0.00758
58/58 - 0s - loss: 0.0025 - mse: 0.0025 - mae: 0.0380 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0683 - lr: 1.0000e-05 - 299ms/epoch - 5ms/step
Epoch 499/500

Epoch 00499: val_loss did not improve from 0.00758
58/58 - 0s - loss: 0.0024 - mse: 0.0024 - mae: 0.0380 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0684 - lr: 1.0000e-05 - 242ms/epoch - 4ms/step
Epoch 500/500

Epoch 00500: val_loss did not improve from 0.00758
58/58 - 0s - loss: 0.0023 - mse: 0.0023 - mae: 0.0375 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0685 - lr: 1.0000e-05 - 245ms/epoch - 4ms/step
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 42.30488040231578 
RMSE:	 6.504220199402522 
MAPE:	 5.010195929360332

DEMA
Prediction vs Close:		55.6% Accuracy
Prediction vs Prediction:	54.48% Accuracy
MSE:	 23.305922116020078 
RMSE:	 4.827620751055335 
MAPE:	 3.7452201197397774

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 18.082341646298453 
RMSE:	 4.252333670621163 
MAPE:	 3.4333194517527637

MIDPOINT
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 91.59813707600279 
RMSE:	 9.57069156727991 
MAPE:	 7.718313236319782
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16837.838, Time=3.70 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14497.319, Time=3.94 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16084.348, Time=6.70 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-15317.920, Time=11.85 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15304.480, Time=11.29 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15949.053, Time=12.81 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17059.707, Time=11.96 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-15313.920, Time=14.47 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16054.952, Time=13.41 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11445.350, Time=34.67 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 124.799 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8561.853
Date:                Sun, 12 Dec 2021   AIC                         -17059.707
Time:                        18:58:22   BIC                         -16909.600
Sample:                             0   HQIC                        -17002.059
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.003e-07   7.69e-05     -0.001      0.999      -0.000       0.000
x2         -1.001e-07   7.44e-05     -0.001      0.999      -0.000       0.000
x3         -1.006e-07   7.84e-05     -0.001      0.999      -0.000       0.000
x4             1.0000   7.11e-05   1.41e+04      0.000       1.000       1.000
x5         -9.611e-08   6.77e-05     -0.001      0.999      -0.000       0.000
x6         -1.249e-07   4.06e-05     -0.003      0.998   -7.96e-05    7.94e-05
x7             -1e-07   7.89e-05     -0.001      0.999      -0.000       0.000
x8            -0.0002   9.43e-05     -1.838      0.066      -0.000    1.15e-05
x9          2.853e-08   9.89e-05      0.000      1.000      -0.000       0.000
x10        -4.022e-05      0.000     -0.200      0.842      -0.000       0.000
x11            0.0003      7e-05      4.122      0.000       0.000       0.000
x12          7.55e-05      0.000      0.633      0.527      -0.000       0.000
x13        -1.005e-07   7.29e-05     -0.001      0.999      -0.000       0.000
x14        -2.756e-07      0.000     -0.001      0.999      -0.000       0.000
x15        -8.419e-08   8.98e-05     -0.001      0.999      -0.000       0.000
x16        -2.171e-07      0.000     -0.001      0.999      -0.000       0.000
x17        -1.105e-07   9.93e-05     -0.001      0.999      -0.000       0.000
x18         1.263e-07   3.22e-05      0.004      0.997   -6.31e-05    6.33e-05
x19        -8.769e-08      0.000     -0.001      0.999      -0.000       0.000
x20        -5.772e-08      0.000     -0.000      1.000      -0.000       0.000
x21         -9.77e-08      0.000     -0.001      1.000      -0.000       0.000
x22        -3.686e-12   7.09e-07   -5.2e-06      1.000   -1.39e-06    1.39e-06
x23        -9.216e-12    2.4e-05  -3.83e-07      1.000   -4.71e-05    4.71e-05
x24        -3.648e-07      0.000     -0.001      0.999      -0.001       0.001
x25        -1.391e-07      0.001     -0.000      1.000      -0.002       0.002
x26        -3.142e-07      0.000     -0.001      0.999      -0.001       0.001
x27        -3.042e-07   5.47e-05     -0.006      0.996      -0.000       0.000
x28        -1.785e-07      0.000     -0.001      0.999      -0.000       0.000
x29        -1.909e-07      0.000     -0.001      1.000      -0.001       0.001
ma.L1         -1.3901   8.24e-06  -1.69e+05      0.000      -1.390      -1.390
ma.L2          0.4035   2.01e-05   2.01e+04      0.000       0.403       0.404
sigma2      7.538e-11   6.94e-11      1.085      0.278   -6.07e-11    2.11e-10
===================================================================================
Ljung-Box (L1) (Q):                  69.36   Jarque-Bera (JB):           6470073.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                           -12.55
Prob(H) (two-sided):                  0.00   Kurtosis:                       441.48
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 3.58e+22. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04050, saving model to LSTM7.h5
43/43 - 3s - loss: 0.0770 - mse: 0.0770 - mae: 0.2109 - val_loss: 0.0405 - val_mse: 0.0405 - val_mae: 0.1509 - lr: 0.0010 - 3s/epoch - 61ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04050
43/43 - 0s - loss: 0.0220 - mse: 0.0220 - mae: 0.1194 - val_loss: 0.0447 - val_mse: 0.0447 - val_mae: 0.1626 - lr: 0.0010 - 223ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.04050 to 0.01821, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0221 - mse: 0.0221 - mae: 0.1186 - val_loss: 0.0182 - val_mse: 0.0182 - val_mae: 0.1018 - lr: 0.0010 - 250ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0231 - mse: 0.0231 - mae: 0.1213 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1161 - lr: 0.0010 - 237ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0167 - mse: 0.0167 - mae: 0.1010 - val_loss: 0.0191 - val_mse: 0.0191 - val_mae: 0.1143 - lr: 0.0010 - 182ms/epoch - 4ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0295 - mse: 0.0295 - mae: 0.1410 - val_loss: 0.0344 - val_mse: 0.0344 - val_mae: 0.1468 - lr: 0.0010 - 198ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0164 - mse: 0.0164 - mae: 0.0990 - val_loss: 0.0503 - val_mse: 0.0503 - val_mae: 0.1967 - lr: 0.0010 - 178ms/epoch - 4ms/step
Epoch 8/500

Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00008: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0357 - mse: 0.0357 - mae: 0.1616 - val_loss: 0.0665 - val_mse: 0.0665 - val_mae: 0.2258 - lr: 0.0010 - 192ms/epoch - 4ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0141 - mse: 0.0141 - mae: 0.0950 - val_loss: 0.0350 - val_mse: 0.0350 - val_mae: 0.1511 - lr: 1.0000e-04 - 200ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0643 - val_loss: 0.0279 - val_mse: 0.0279 - val_mae: 0.1326 - lr: 1.0000e-04 - 180ms/epoch - 4ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0069 - mse: 0.0069 - mae: 0.0650 - val_loss: 0.0237 - val_mse: 0.0237 - val_mae: 0.1212 - lr: 1.0000e-04 - 193ms/epoch - 4ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0617 - val_loss: 0.0214 - val_mse: 0.0214 - val_mae: 0.1146 - lr: 1.0000e-04 - 181ms/epoch - 4ms/step
Epoch 13/500

Epoch 00013: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00013: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0611 - val_loss: 0.0194 - val_mse: 0.0194 - val_mae: 0.1088 - lr: 1.0000e-04 - 184ms/epoch - 4ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0626 - val_loss: 0.0193 - val_mse: 0.0193 - val_mae: 0.1085 - lr: 1.0000e-05 - 200ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0619 - val_loss: 0.0192 - val_mse: 0.0192 - val_mae: 0.1083 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0636 - val_loss: 0.0190 - val_mse: 0.0190 - val_mae: 0.1077 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0064 - mse: 0.0064 - mae: 0.0626 - val_loss: 0.0188 - val_mse: 0.0188 - val_mae: 0.1071 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00018: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0603 - val_loss: 0.0187 - val_mse: 0.0187 - val_mae: 0.1067 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0605 - val_loss: 0.0185 - val_mse: 0.0185 - val_mae: 0.1063 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.01821
43/43 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0598 - val_loss: 0.0183 - val_mse: 0.0183 - val_mae: 0.1057 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 21/500

Epoch 00021: val_loss improved from 0.01821 to 0.01815, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0612 - val_loss: 0.0181 - val_mse: 0.0181 - val_mae: 0.1053 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 22/500

Epoch 00022: val_loss improved from 0.01815 to 0.01805, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0603 - val_loss: 0.0180 - val_mse: 0.0180 - val_mae: 0.1050 - lr: 1.0000e-05 - 240ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss improved from 0.01805 to 0.01794, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0611 - val_loss: 0.0179 - val_mse: 0.0179 - val_mae: 0.1047 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss improved from 0.01794 to 0.01781, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0617 - val_loss: 0.0178 - val_mse: 0.0178 - val_mae: 0.1043 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss improved from 0.01781 to 0.01755, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0607 - val_loss: 0.0176 - val_mse: 0.0176 - val_mae: 0.1036 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss improved from 0.01755 to 0.01736, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0589 - val_loss: 0.0174 - val_mse: 0.0174 - val_mae: 0.1030 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss improved from 0.01736 to 0.01717, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0606 - val_loss: 0.0172 - val_mse: 0.0172 - val_mae: 0.1025 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss improved from 0.01717 to 0.01703, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0616 - val_loss: 0.0170 - val_mse: 0.0170 - val_mae: 0.1021 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss improved from 0.01703 to 0.01692, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0617 - val_loss: 0.0169 - val_mse: 0.0169 - val_mae: 0.1018 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 30/500

Epoch 00030: val_loss improved from 0.01692 to 0.01675, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0063 - mse: 0.0063 - mae: 0.0618 - val_loss: 0.0168 - val_mse: 0.0168 - val_mae: 0.1012 - lr: 1.0000e-05 - 237ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss improved from 0.01675 to 0.01659, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0628 - val_loss: 0.0166 - val_mse: 0.0166 - val_mae: 0.1007 - lr: 1.0000e-05 - 242ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss improved from 0.01659 to 0.01650, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0060 - mse: 0.0060 - mae: 0.0592 - val_loss: 0.0165 - val_mse: 0.0165 - val_mae: 0.1004 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss improved from 0.01650 to 0.01635, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0562 - val_loss: 0.0163 - val_mse: 0.0163 - val_mae: 0.1000 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss improved from 0.01635 to 0.01617, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0575 - val_loss: 0.0162 - val_mse: 0.0162 - val_mae: 0.0995 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss improved from 0.01617 to 0.01615, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0587 - val_loss: 0.0162 - val_mse: 0.0162 - val_mae: 0.0994 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.01615
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0603 - val_loss: 0.0162 - val_mse: 0.0162 - val_mae: 0.0995 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss improved from 0.01615 to 0.01592, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0603 - val_loss: 0.0159 - val_mse: 0.0159 - val_mae: 0.0986 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss improved from 0.01592 to 0.01564, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0588 - val_loss: 0.0156 - val_mse: 0.0156 - val_mae: 0.0978 - lr: 1.0000e-05 - 210ms/epoch - 5ms/step
Epoch 39/500

Epoch 00039: val_loss improved from 0.01564 to 0.01561, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0062 - mse: 0.0062 - mae: 0.0615 - val_loss: 0.0156 - val_mse: 0.0156 - val_mae: 0.0977 - lr: 1.0000e-05 - 196ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss improved from 0.01561 to 0.01549, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0589 - val_loss: 0.0155 - val_mse: 0.0155 - val_mae: 0.0973 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.01549
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0576 - val_loss: 0.0156 - val_mse: 0.0156 - val_mae: 0.0975 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 42/500

Epoch 00042: val_loss improved from 0.01549 to 0.01544, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0592 - val_loss: 0.0154 - val_mse: 0.0154 - val_mae: 0.0971 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss improved from 0.01544 to 0.01533, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0561 - val_loss: 0.0153 - val_mse: 0.0153 - val_mae: 0.0967 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss improved from 0.01533 to 0.01516, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0058 - mse: 0.0058 - mae: 0.0599 - val_loss: 0.0152 - val_mse: 0.0152 - val_mae: 0.0962 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss improved from 0.01516 to 0.01501, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0582 - val_loss: 0.0150 - val_mse: 0.0150 - val_mae: 0.0957 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss improved from 0.01501 to 0.01486, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0585 - val_loss: 0.0149 - val_mse: 0.0149 - val_mae: 0.0952 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.01486
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0587 - val_loss: 0.0149 - val_mse: 0.0149 - val_mae: 0.0952 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.01486
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0577 - val_loss: 0.0150 - val_mse: 0.0150 - val_mae: 0.0955 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.01486
43/43 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0588 - val_loss: 0.0149 - val_mse: 0.0149 - val_mae: 0.0954 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss improved from 0.01486 to 0.01481, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0061 - mse: 0.0061 - mae: 0.0617 - val_loss: 0.0148 - val_mse: 0.0148 - val_mae: 0.0950 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss improved from 0.01481 to 0.01477, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0574 - val_loss: 0.0148 - val_mse: 0.0148 - val_mae: 0.0948 - lr: 1.0000e-05 - 239ms/epoch - 6ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.01477
43/43 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0566 - val_loss: 0.0149 - val_mse: 0.0149 - val_mae: 0.0952 - lr: 1.0000e-05 - 202ms/epoch - 5ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.01477
43/43 - 0s - loss: 0.0059 - mse: 0.0059 - mae: 0.0591 - val_loss: 0.0148 - val_mse: 0.0148 - val_mae: 0.0949 - lr: 1.0000e-05 - 210ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss improved from 0.01477 to 0.01456, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0561 - val_loss: 0.0146 - val_mse: 0.0146 - val_mae: 0.0942 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.01456
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0558 - val_loss: 0.0146 - val_mse: 0.0146 - val_mae: 0.0943 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss improved from 0.01456 to 0.01444, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0577 - val_loss: 0.0144 - val_mse: 0.0144 - val_mae: 0.0938 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss improved from 0.01444 to 0.01443, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0571 - val_loss: 0.0144 - val_mse: 0.0144 - val_mae: 0.0937 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 58/500

Epoch 00058: val_loss improved from 0.01443 to 0.01425, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0583 - val_loss: 0.0143 - val_mse: 0.0143 - val_mae: 0.0931 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 59/500

Epoch 00059: val_loss did not improve from 0.01425
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0582 - val_loss: 0.0143 - val_mse: 0.0143 - val_mae: 0.0932 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 60/500

Epoch 00060: val_loss improved from 0.01425 to 0.01413, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0551 - val_loss: 0.0141 - val_mse: 0.0141 - val_mae: 0.0927 - lr: 1.0000e-05 - 200ms/epoch - 5ms/step
Epoch 61/500

Epoch 00061: val_loss improved from 0.01413 to 0.01405, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0581 - val_loss: 0.0140 - val_mse: 0.0140 - val_mae: 0.0924 - lr: 1.0000e-05 - 203ms/epoch - 5ms/step
Epoch 62/500

Epoch 00062: val_loss improved from 0.01405 to 0.01401, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0564 - val_loss: 0.0140 - val_mse: 0.0140 - val_mae: 0.0923 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 63/500

Epoch 00063: val_loss improved from 0.01401 to 0.01390, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0579 - val_loss: 0.0139 - val_mse: 0.0139 - val_mae: 0.0920 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 64/500

Epoch 00064: val_loss improved from 0.01390 to 0.01384, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0588 - val_loss: 0.0138 - val_mse: 0.0138 - val_mae: 0.0918 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 65/500

Epoch 00065: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0562 - val_loss: 0.0139 - val_mse: 0.0139 - val_mae: 0.0920 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 66/500

Epoch 00066: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0554 - val_loss: 0.0139 - val_mse: 0.0139 - val_mae: 0.0919 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 67/500

Epoch 00067: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0573 - val_loss: 0.0140 - val_mse: 0.0140 - val_mae: 0.0921 - lr: 1.0000e-05 - 197ms/epoch - 5ms/step
Epoch 68/500

Epoch 00068: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0571 - val_loss: 0.0139 - val_mse: 0.0139 - val_mae: 0.0920 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 69/500

Epoch 00069: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0581 - val_loss: 0.0138 - val_mse: 0.0138 - val_mae: 0.0917 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 70/500

Epoch 00070: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0057 - mse: 0.0057 - mae: 0.0587 - val_loss: 0.0139 - val_mse: 0.0139 - val_mae: 0.0917 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 71/500

Epoch 00071: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0554 - val_loss: 0.0140 - val_mse: 0.0140 - val_mae: 0.0921 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 72/500

Epoch 00072: val_loss did not improve from 0.01384
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0556 - val_loss: 0.0138 - val_mse: 0.0138 - val_mae: 0.0916 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 73/500

Epoch 00073: val_loss improved from 0.01384 to 0.01367, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0565 - val_loss: 0.0137 - val_mse: 0.0137 - val_mae: 0.0911 - lr: 1.0000e-05 - 240ms/epoch - 6ms/step
Epoch 74/500

Epoch 00074: val_loss did not improve from 0.01367
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0569 - val_loss: 0.0138 - val_mse: 0.0138 - val_mae: 0.0915 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 75/500

Epoch 00075: val_loss did not improve from 0.01367
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0564 - val_loss: 0.0138 - val_mse: 0.0138 - val_mae: 0.0914 - lr: 1.0000e-05 - 177ms/epoch - 4ms/step
Epoch 76/500

Epoch 00076: val_loss improved from 0.01367 to 0.01366, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0055 - mse: 0.0055 - mae: 0.0572 - val_loss: 0.0137 - val_mse: 0.0137 - val_mae: 0.0910 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 77/500

Epoch 00077: val_loss did not improve from 0.01366
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0547 - val_loss: 0.0137 - val_mse: 0.0137 - val_mae: 0.0910 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 78/500

Epoch 00078: val_loss did not improve from 0.01366
43/43 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0566 - val_loss: 0.0137 - val_mse: 0.0137 - val_mae: 0.0909 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 79/500

Epoch 00079: val_loss improved from 0.01366 to 0.01358, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0544 - val_loss: 0.0136 - val_mse: 0.0136 - val_mae: 0.0906 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 80/500

Epoch 00080: val_loss improved from 0.01358 to 0.01344, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0540 - val_loss: 0.0134 - val_mse: 0.0134 - val_mae: 0.0901 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 81/500

Epoch 00081: val_loss improved from 0.01344 to 0.01325, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0554 - val_loss: 0.0132 - val_mse: 0.0132 - val_mae: 0.0895 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 82/500

Epoch 00082: val_loss improved from 0.01325 to 0.01313, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0576 - val_loss: 0.0131 - val_mse: 0.0131 - val_mae: 0.0891 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 83/500

Epoch 00083: val_loss improved from 0.01313 to 0.01301, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0559 - val_loss: 0.0130 - val_mse: 0.0130 - val_mae: 0.0888 - lr: 1.0000e-05 - 199ms/epoch - 5ms/step
Epoch 84/500

Epoch 00084: val_loss improved from 0.01301 to 0.01299, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0557 - val_loss: 0.0130 - val_mse: 0.0130 - val_mae: 0.0887 - lr: 1.0000e-05 - 197ms/epoch - 5ms/step
Epoch 85/500

Epoch 00085: val_loss did not improve from 0.01299
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0544 - val_loss: 0.0130 - val_mse: 0.0130 - val_mae: 0.0888 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 86/500

Epoch 00086: val_loss improved from 0.01299 to 0.01282, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0550 - val_loss: 0.0128 - val_mse: 0.0128 - val_mae: 0.0881 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 87/500

Epoch 00087: val_loss did not improve from 0.01282
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0549 - val_loss: 0.0128 - val_mse: 0.0128 - val_mae: 0.0882 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 88/500

Epoch 00088: val_loss did not improve from 0.01282
43/43 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0560 - val_loss: 0.0129 - val_mse: 0.0129 - val_mae: 0.0883 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 89/500

Epoch 00089: val_loss did not improve from 0.01282
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0532 - val_loss: 0.0129 - val_mse: 0.0129 - val_mae: 0.0882 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 90/500

Epoch 00090: val_loss did not improve from 0.01282
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0569 - val_loss: 0.0129 - val_mse: 0.0129 - val_mae: 0.0882 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 91/500

Epoch 00091: val_loss improved from 0.01282 to 0.01271, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0546 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0877 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 92/500

Epoch 00092: val_loss improved from 0.01271 to 0.01270, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0542 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0877 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 93/500

Epoch 00093: val_loss improved from 0.01270 to 0.01266, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0557 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0875 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 94/500

Epoch 00094: val_loss did not improve from 0.01266
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0558 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0875 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 95/500

Epoch 00095: val_loss improved from 0.01266 to 0.01258, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0540 - val_loss: 0.0126 - val_mse: 0.0126 - val_mae: 0.0872 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 96/500

Epoch 00096: val_loss did not improve from 0.01258
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0519 - val_loss: 0.0127 - val_mse: 0.0127 - val_mae: 0.0874 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 97/500

Epoch 00097: val_loss did not improve from 0.01258
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0548 - val_loss: 0.0126 - val_mse: 0.0126 - val_mae: 0.0872 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 98/500

Epoch 00098: val_loss improved from 0.01258 to 0.01236, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0561 - val_loss: 0.0124 - val_mse: 0.0124 - val_mae: 0.0864 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 99/500

Epoch 00099: val_loss improved from 0.01236 to 0.01215, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0568 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0859 - lr: 1.0000e-05 - 201ms/epoch - 5ms/step
Epoch 100/500

Epoch 00100: val_loss improved from 0.01215 to 0.01210, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0545 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0857 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 101/500

Epoch 00101: val_loss did not improve from 0.01210
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0547 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0857 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 102/500

Epoch 00102: val_loss did not improve from 0.01210
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0534 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0857 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 103/500

Epoch 00103: val_loss improved from 0.01210 to 0.01206, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0053 - mse: 0.0053 - mae: 0.0572 - val_loss: 0.0121 - val_mse: 0.0121 - val_mae: 0.0855 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 104/500

Epoch 00104: val_loss improved from 0.01206 to 0.01204, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0550 - val_loss: 0.0120 - val_mse: 0.0120 - val_mae: 0.0855 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 105/500

Epoch 00105: val_loss improved from 0.01204 to 0.01196, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0544 - val_loss: 0.0120 - val_mse: 0.0120 - val_mae: 0.0852 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 106/500

Epoch 00106: val_loss improved from 0.01196 to 0.01195, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0537 - val_loss: 0.0120 - val_mse: 0.0120 - val_mae: 0.0852 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 107/500

Epoch 00107: val_loss improved from 0.01195 to 0.01194, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0528 - val_loss: 0.0119 - val_mse: 0.0119 - val_mae: 0.0851 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 108/500

Epoch 00108: val_loss did not improve from 0.01194
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0535 - val_loss: 0.0120 - val_mse: 0.0120 - val_mae: 0.0852 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 109/500

Epoch 00109: val_loss did not improve from 0.01194
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0530 - val_loss: 0.0120 - val_mse: 0.0120 - val_mae: 0.0854 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 110/500

Epoch 00110: val_loss improved from 0.01194 to 0.01190, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0516 - val_loss: 0.0119 - val_mse: 0.0119 - val_mae: 0.0850 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 111/500

Epoch 00111: val_loss improved from 0.01190 to 0.01183, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0546 - val_loss: 0.0118 - val_mse: 0.0118 - val_mae: 0.0848 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 112/500

Epoch 00112: val_loss did not improve from 0.01183
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0535 - val_loss: 0.0119 - val_mse: 0.0119 - val_mae: 0.0849 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 113/500

Epoch 00113: val_loss improved from 0.01183 to 0.01178, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0539 - val_loss: 0.0118 - val_mse: 0.0118 - val_mae: 0.0845 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 114/500

Epoch 00114: val_loss improved from 0.01178 to 0.01175, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0545 - val_loss: 0.0118 - val_mse: 0.0118 - val_mae: 0.0844 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 115/500

Epoch 00115: val_loss improved from 0.01175 to 0.01168, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0525 - val_loss: 0.0117 - val_mse: 0.0117 - val_mae: 0.0842 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 116/500

Epoch 00116: val_loss improved from 0.01168 to 0.01154, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0554 - val_loss: 0.0115 - val_mse: 0.0115 - val_mae: 0.0838 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 117/500

Epoch 00117: val_loss improved from 0.01154 to 0.01131, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0542 - val_loss: 0.0113 - val_mse: 0.0113 - val_mae: 0.0832 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 118/500

Epoch 00118: val_loss did not improve from 0.01131
43/43 - 0s - loss: 0.0054 - mse: 0.0054 - mae: 0.0562 - val_loss: 0.0113 - val_mse: 0.0113 - val_mae: 0.0831 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 119/500

Epoch 00119: val_loss improved from 0.01131 to 0.01127, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0544 - val_loss: 0.0113 - val_mse: 0.0113 - val_mae: 0.0830 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 120/500

Epoch 00120: val_loss did not improve from 0.01127
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0543 - val_loss: 0.0114 - val_mse: 0.0114 - val_mae: 0.0833 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 121/500

Epoch 00121: val_loss did not improve from 0.01127
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0507 - val_loss: 0.0116 - val_mse: 0.0116 - val_mae: 0.0837 - lr: 1.0000e-05 - 180ms/epoch - 4ms/step
Epoch 122/500

Epoch 00122: val_loss did not improve from 0.01127
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0522 - val_loss: 0.0114 - val_mse: 0.0114 - val_mae: 0.0833 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 123/500

Epoch 00123: val_loss improved from 0.01127 to 0.01116, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0511 - val_loss: 0.0112 - val_mse: 0.0112 - val_mae: 0.0825 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 124/500

Epoch 00124: val_loss improved from 0.01116 to 0.01114, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0111 - val_mse: 0.0111 - val_mae: 0.0825 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 125/500

Epoch 00125: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0529 - val_loss: 0.0113 - val_mse: 0.0113 - val_mae: 0.0828 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 126/500

Epoch 00126: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0490 - val_loss: 0.0116 - val_mse: 0.0116 - val_mae: 0.0835 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 127/500

Epoch 00127: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0528 - val_loss: 0.0115 - val_mse: 0.0115 - val_mae: 0.0833 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 128/500

Epoch 00128: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0535 - val_loss: 0.0116 - val_mse: 0.0116 - val_mae: 0.0834 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 129/500

Epoch 00129: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0524 - val_loss: 0.0115 - val_mse: 0.0115 - val_mae: 0.0830 - lr: 1.0000e-05 - 240ms/epoch - 6ms/step
Epoch 130/500

Epoch 00130: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0538 - val_loss: 0.0116 - val_mse: 0.0116 - val_mae: 0.0833 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 131/500

Epoch 00131: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0539 - val_loss: 0.0114 - val_mse: 0.0114 - val_mae: 0.0827 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 132/500

Epoch 00132: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0505 - val_loss: 0.0114 - val_mse: 0.0114 - val_mae: 0.0827 - lr: 1.0000e-05 - 200ms/epoch - 5ms/step
Epoch 133/500

Epoch 00133: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0046 - mse: 0.0046 - mae: 0.0537 - val_loss: 0.0115 - val_mse: 0.0115 - val_mae: 0.0828 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 134/500

Epoch 00134: val_loss did not improve from 0.01114
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0529 - val_loss: 0.0113 - val_mse: 0.0113 - val_mae: 0.0822 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 135/500

Epoch 00135: val_loss improved from 0.01114 to 0.01112, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0509 - val_loss: 0.0111 - val_mse: 0.0111 - val_mae: 0.0817 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 136/500

Epoch 00136: val_loss improved from 0.01112 to 0.01110, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0536 - val_loss: 0.0111 - val_mse: 0.0111 - val_mae: 0.0816 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 137/500

Epoch 00137: val_loss improved from 0.01110 to 0.01092, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0540 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0810 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 138/500

Epoch 00138: val_loss improved from 0.01092 to 0.01086, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0517 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0808 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 139/500

Epoch 00139: val_loss improved from 0.01086 to 0.01068, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0510 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0803 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 140/500

Epoch 00140: val_loss improved from 0.01068 to 0.01065, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0528 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0802 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 141/500

Epoch 00141: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0048 - mse: 0.0048 - mae: 0.0544 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0806 - lr: 1.0000e-05 - 178ms/epoch - 4ms/step
Epoch 142/500

Epoch 00142: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0519 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0805 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 143/500

Epoch 00143: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0505 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0808 - lr: 1.0000e-05 - 199ms/epoch - 5ms/step
Epoch 144/500

Epoch 00144: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0507 - val_loss: 0.0110 - val_mse: 0.0110 - val_mae: 0.0812 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 145/500

Epoch 00145: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0519 - val_loss: 0.0110 - val_mse: 0.0110 - val_mae: 0.0810 - lr: 1.0000e-05 - 178ms/epoch - 4ms/step
Epoch 146/500

Epoch 00146: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0510 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0807 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 147/500

Epoch 00147: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0497 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0804 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 148/500

Epoch 00148: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0506 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0806 - lr: 1.0000e-05 - 210ms/epoch - 5ms/step
Epoch 149/500

Epoch 00149: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0520 - val_loss: 0.0111 - val_mse: 0.0111 - val_mae: 0.0812 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 150/500

Epoch 00150: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0516 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0805 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 151/500

Epoch 00151: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0488 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0804 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 152/500

Epoch 00152: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0805 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 153/500

Epoch 00153: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0491 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0802 - lr: 1.0000e-05 - 188ms/epoch - 4ms/step
Epoch 154/500

Epoch 00154: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0527 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0804 - lr: 1.0000e-05 - 180ms/epoch - 4ms/step
Epoch 155/500

Epoch 00155: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0524 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0803 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 156/500

Epoch 00156: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0507 - val_loss: 0.0110 - val_mse: 0.0110 - val_mae: 0.0807 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 157/500

Epoch 00157: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0498 - val_loss: 0.0110 - val_mse: 0.0110 - val_mae: 0.0808 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 158/500

Epoch 00158: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0110 - val_mse: 0.0110 - val_mae: 0.0807 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 159/500

Epoch 00159: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0111 - val_mse: 0.0111 - val_mae: 0.0809 - lr: 1.0000e-05 - 176ms/epoch - 4ms/step
Epoch 160/500

Epoch 00160: val_loss did not improve from 0.01065
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0507 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0802 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 161/500

Epoch 00161: val_loss improved from 0.01065 to 0.01060, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0513 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0792 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 162/500

Epoch 00162: val_loss improved from 0.01060 to 0.01048, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0486 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0788 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 163/500

Epoch 00163: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0049 - mse: 0.0049 - mae: 0.0551 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0791 - lr: 1.0000e-05 - 186ms/epoch - 4ms/step
Epoch 164/500

Epoch 00164: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0506 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0796 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 165/500

Epoch 00165: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0510 - val_loss: 0.0109 - val_mse: 0.0109 - val_mae: 0.0800 - lr: 1.0000e-05 - 178ms/epoch - 4ms/step
Epoch 166/500

Epoch 00166: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0505 - val_loss: 0.0108 - val_mse: 0.0108 - val_mae: 0.0798 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 167/500

Epoch 00167: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0518 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0792 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 168/500

Epoch 00168: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0512 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0791 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 169/500

Epoch 00169: val_loss did not improve from 0.01048
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0514 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0790 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 170/500

Epoch 00170: val_loss improved from 0.01048 to 0.01036, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0104 - val_mse: 0.0104 - val_mae: 0.0781 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 171/500

Epoch 00171: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0504 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0786 - lr: 1.0000e-05 - 206ms/epoch - 5ms/step
Epoch 172/500

Epoch 00172: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0501 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0791 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 173/500

Epoch 00173: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0490 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0788 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 174/500

Epoch 00174: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0513 - val_loss: 0.0106 - val_mse: 0.0106 - val_mae: 0.0790 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 175/500

Epoch 00175: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0510 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0784 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 176/500

Epoch 00176: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0471 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0783 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 177/500

Epoch 00177: val_loss did not improve from 0.01036
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0501 - val_loss: 0.0104 - val_mse: 0.0104 - val_mae: 0.0781 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 178/500

Epoch 00178: val_loss improved from 0.01036 to 0.01027, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0497 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0776 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 179/500

Epoch 00179: val_loss improved from 0.01027 to 0.01020, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0540 - val_loss: 0.0102 - val_mse: 0.0102 - val_mae: 0.0773 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 180/500

Epoch 00180: val_loss did not improve from 0.01020
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0502 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0778 - lr: 1.0000e-05 - 197ms/epoch - 5ms/step
Epoch 181/500

Epoch 00181: val_loss did not improve from 0.01020
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0477 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0783 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 182/500

Epoch 00182: val_loss did not improve from 0.01020
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0480 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0791 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 183/500

Epoch 00183: val_loss did not improve from 0.01020
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0494 - val_loss: 0.0107 - val_mse: 0.0107 - val_mae: 0.0790 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 184/500

Epoch 00184: val_loss did not improve from 0.01020
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0510 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0782 - lr: 1.0000e-05 - 188ms/epoch - 4ms/step
Epoch 185/500

Epoch 00185: val_loss did not improve from 0.01020
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0514 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0774 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 186/500

Epoch 00186: val_loss improved from 0.01020 to 0.01009, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0510 - val_loss: 0.0101 - val_mse: 0.0101 - val_mae: 0.0768 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 187/500

Epoch 00187: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0491 - val_loss: 0.0101 - val_mse: 0.0101 - val_mae: 0.0769 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 188/500

Epoch 00188: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0498 - val_loss: 0.0102 - val_mse: 0.0102 - val_mae: 0.0772 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 189/500

Epoch 00189: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0474 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0780 - lr: 1.0000e-05 - 190ms/epoch - 4ms/step
Epoch 190/500

Epoch 00190: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0481 - val_loss: 0.0105 - val_mse: 0.0105 - val_mae: 0.0780 - lr: 1.0000e-05 - 237ms/epoch - 6ms/step
Epoch 191/500

Epoch 00191: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0501 - val_loss: 0.0104 - val_mse: 0.0104 - val_mae: 0.0776 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 192/500

Epoch 00192: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0490 - val_loss: 0.0103 - val_mse: 0.0103 - val_mae: 0.0772 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 193/500

Epoch 00193: val_loss did not improve from 0.01009
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0502 - val_loss: 0.0102 - val_mse: 0.0102 - val_mae: 0.0769 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 194/500

Epoch 00194: val_loss improved from 0.01009 to 0.00989, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0481 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0758 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 195/500

Epoch 00195: val_loss improved from 0.00989 to 0.00975, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0474 - val_loss: 0.0097 - val_mse: 0.0097 - val_mae: 0.0754 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 196/500

Epoch 00196: val_loss did not improve from 0.00975
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0498 - val_loss: 0.0098 - val_mse: 0.0098 - val_mae: 0.0756 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 197/500

Epoch 00197: val_loss did not improve from 0.00975
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0494 - val_loss: 0.0099 - val_mse: 0.0099 - val_mae: 0.0759 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 198/500

Epoch 00198: val_loss did not improve from 0.00975
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0499 - val_loss: 0.0098 - val_mse: 0.0098 - val_mae: 0.0756 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 199/500

Epoch 00199: val_loss improved from 0.00975 to 0.00951, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0517 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0745 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 200/500

Epoch 00200: val_loss improved from 0.00951 to 0.00950, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0491 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0745 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 201/500

Epoch 00201: val_loss did not improve from 0.00950
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0469 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0744 - lr: 1.0000e-05 - 188ms/epoch - 4ms/step
Epoch 202/500

Epoch 00202: val_loss did not improve from 0.00950
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0492 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0744 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 203/500

Epoch 00203: val_loss improved from 0.00950 to 0.00941, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0507 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0740 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 204/500

Epoch 00204: val_loss did not improve from 0.00941
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0484 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0743 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 205/500

Epoch 00205: val_loss did not improve from 0.00941
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0475 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0744 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 206/500

Epoch 00206: val_loss improved from 0.00941 to 0.00929, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0482 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0737 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 207/500

Epoch 00207: val_loss did not improve from 0.00929
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0476 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0737 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 208/500

Epoch 00208: val_loss improved from 0.00929 to 0.00922, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0464 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0734 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 209/500

Epoch 00209: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0487 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0735 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 210/500

Epoch 00210: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0488 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0734 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 211/500

Epoch 00211: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0499 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0737 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 212/500

Epoch 00212: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0498 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0736 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 213/500

Epoch 00213: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0485 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0734 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 214/500

Epoch 00214: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0492 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0737 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 215/500

Epoch 00215: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0507 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0735 - lr: 1.0000e-05 - 194ms/epoch - 5ms/step
Epoch 216/500

Epoch 00216: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0506 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0733 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 217/500

Epoch 00217: val_loss improved from 0.00922 to 0.00922, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0477 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0732 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 218/500

Epoch 00218: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0494 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0733 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 219/500

Epoch 00219: val_loss did not improve from 0.00922
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0478 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0734 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 220/500

Epoch 00220: val_loss improved from 0.00922 to 0.00917, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0488 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0729 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 221/500

Epoch 00221: val_loss improved from 0.00917 to 0.00906, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0500 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0725 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 222/500

Epoch 00222: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0473 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0726 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 223/500

Epoch 00223: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0504 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0726 - lr: 1.0000e-05 - 203ms/epoch - 5ms/step
Epoch 224/500

Epoch 00224: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0491 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0726 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 225/500

Epoch 00225: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0490 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0730 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 226/500

Epoch 00226: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0498 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0728 - lr: 1.0000e-05 - 198ms/epoch - 5ms/step
Epoch 227/500

Epoch 00227: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0493 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0729 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 228/500

Epoch 00228: val_loss did not improve from 0.00906
43/43 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0515 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0725 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 229/500

Epoch 00229: val_loss improved from 0.00906 to 0.00901, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0479 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0721 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 230/500

Epoch 00230: val_loss did not improve from 0.00901
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0460 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0723 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 231/500

Epoch 00231: val_loss improved from 0.00901 to 0.00893, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0718 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 232/500

Epoch 00232: val_loss improved from 0.00893 to 0.00890, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0500 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0717 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 233/500

Epoch 00233: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0484 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0716 - lr: 1.0000e-05 - 214ms/epoch - 5ms/step
Epoch 234/500

Epoch 00234: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0460 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0719 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 235/500

Epoch 00235: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0479 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0717 - lr: 1.0000e-05 - 200ms/epoch - 5ms/step
Epoch 236/500

Epoch 00236: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0476 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0722 - lr: 1.0000e-05 - 202ms/epoch - 5ms/step
Epoch 237/500

Epoch 00237: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0474 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0725 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 238/500

Epoch 00238: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0486 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0726 - lr: 1.0000e-05 - 198ms/epoch - 5ms/step
Epoch 239/500

Epoch 00239: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0468 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0731 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 240/500

Epoch 00240: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0471 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0736 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 241/500

Epoch 00241: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0472 - val_loss: 0.0096 - val_mse: 0.0096 - val_mae: 0.0741 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 242/500

Epoch 00242: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0446 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0738 - lr: 1.0000e-05 - 198ms/epoch - 5ms/step
Epoch 243/500

Epoch 00243: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0481 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0739 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 244/500

Epoch 00244: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0486 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0736 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 245/500

Epoch 00245: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0474 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0735 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 246/500

Epoch 00246: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0466 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0725 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 247/500

Epoch 00247: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0491 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0730 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 248/500

Epoch 00248: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0467 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0732 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 249/500

Epoch 00249: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0485 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0731 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 250/500

Epoch 00250: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0468 - val_loss: 0.0095 - val_mse: 0.0095 - val_mae: 0.0735 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 251/500

Epoch 00251: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0484 - val_loss: 0.0094 - val_mse: 0.0094 - val_mae: 0.0729 - lr: 1.0000e-05 - 245ms/epoch - 6ms/step
Epoch 252/500

Epoch 00252: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0467 - val_loss: 0.0093 - val_mse: 0.0093 - val_mae: 0.0726 - lr: 1.0000e-05 - 180ms/epoch - 4ms/step
Epoch 253/500

Epoch 00253: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0465 - val_loss: 0.0092 - val_mse: 0.0092 - val_mae: 0.0722 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 254/500

Epoch 00254: val_loss did not improve from 0.00890
43/43 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0510 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0712 - lr: 1.0000e-05 - 177ms/epoch - 4ms/step
Epoch 255/500

Epoch 00255: val_loss improved from 0.00890 to 0.00889, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0464 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0710 - lr: 1.0000e-05 - 242ms/epoch - 6ms/step
Epoch 256/500

Epoch 00256: val_loss did not improve from 0.00889
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0488 - val_loss: 0.0091 - val_mse: 0.0091 - val_mae: 0.0719 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 257/500

Epoch 00257: val_loss did not improve from 0.00889
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0477 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0713 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 258/500

Epoch 00258: val_loss did not improve from 0.00889
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0467 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0712 - lr: 1.0000e-05 - 196ms/epoch - 5ms/step
Epoch 259/500

Epoch 00259: val_loss improved from 0.00889 to 0.00887, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0489 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0709 - lr: 1.0000e-05 - 259ms/epoch - 6ms/step
Epoch 260/500

Epoch 00260: val_loss did not improve from 0.00887
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0483 - val_loss: 0.0090 - val_mse: 0.0090 - val_mae: 0.0712 - lr: 1.0000e-05 - 177ms/epoch - 4ms/step
Epoch 261/500

Epoch 00261: val_loss did not improve from 0.00887
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0460 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0709 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 262/500

Epoch 00262: val_loss improved from 0.00887 to 0.00884, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0471 - val_loss: 0.0088 - val_mse: 0.0088 - val_mae: 0.0707 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 263/500

Epoch 00263: val_loss improved from 0.00884 to 0.00868, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0459 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0701 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 264/500

Epoch 00264: val_loss did not improve from 0.00868
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0476 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0702 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 265/500

Epoch 00265: val_loss did not improve from 0.00868
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0474 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0702 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 266/500

Epoch 00266: val_loss improved from 0.00868 to 0.00855, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0470 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0696 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 267/500

Epoch 00267: val_loss improved from 0.00855 to 0.00846, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0467 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0693 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 268/500

Epoch 00268: val_loss improved from 0.00846 to 0.00844, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0470 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0693 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 269/500

Epoch 00269: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0459 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0697 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 270/500

Epoch 00270: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0483 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0695 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 271/500

Epoch 00271: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0475 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0698 - lr: 1.0000e-05 - 176ms/epoch - 4ms/step
Epoch 272/500

Epoch 00272: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0481 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0695 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 273/500

Epoch 00273: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0426 - val_loss: 0.0089 - val_mse: 0.0089 - val_mae: 0.0709 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 274/500

Epoch 00274: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0458 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0702 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 275/500

Epoch 00275: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0454 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0701 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 276/500

Epoch 00276: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0479 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0696 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 277/500

Epoch 00277: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0471 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0697 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 278/500

Epoch 00278: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0475 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0699 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 279/500

Epoch 00279: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0460 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0700 - lr: 1.0000e-05 - 196ms/epoch - 5ms/step
Epoch 280/500

Epoch 00280: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0695 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 281/500

Epoch 00281: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0488 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0696 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 282/500

Epoch 00282: val_loss did not improve from 0.00844
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0477 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0691 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 283/500

Epoch 00283: val_loss improved from 0.00844 to 0.00837, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0469 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0687 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 284/500

Epoch 00284: val_loss did not improve from 0.00837
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0476 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0690 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 285/500

Epoch 00285: val_loss did not improve from 0.00837
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0466 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0695 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 286/500

Epoch 00286: val_loss did not improve from 0.00837
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0456 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0687 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 287/500

Epoch 00287: val_loss improved from 0.00837 to 0.00818, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0485 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0679 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 288/500

Epoch 00288: val_loss improved from 0.00818 to 0.00815, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0492 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0678 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 289/500

Epoch 00289: val_loss did not improve from 0.00815
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0453 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0683 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 290/500

Epoch 00290: val_loss did not improve from 0.00815
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0491 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0683 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 291/500

Epoch 00291: val_loss improved from 0.00815 to 0.00810, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0458 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0677 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 292/500

Epoch 00292: val_loss improved from 0.00810 to 0.00798, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0466 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0673 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 293/500

Epoch 00293: val_loss did not improve from 0.00798
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0473 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0678 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 294/500

Epoch 00294: val_loss did not improve from 0.00798
43/43 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0483 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0675 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 295/500

Epoch 00295: val_loss improved from 0.00798 to 0.00792, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0462 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0670 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 296/500

Epoch 00296: val_loss did not improve from 0.00792
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0471 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0671 - lr: 1.0000e-05 - 226ms/epoch - 5ms/step
Epoch 297/500

Epoch 00297: val_loss improved from 0.00792 to 0.00790, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0477 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0670 - lr: 1.0000e-05 - 202ms/epoch - 5ms/step
Epoch 298/500

Epoch 00298: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0464 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0670 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 299/500

Epoch 00299: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0479 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0675 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 300/500

Epoch 00300: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0459 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0678 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 301/500

Epoch 00301: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0458 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0671 - lr: 1.0000e-05 - 200ms/epoch - 5ms/step
Epoch 302/500

Epoch 00302: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0463 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0669 - lr: 1.0000e-05 - 178ms/epoch - 4ms/step
Epoch 303/500

Epoch 00303: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0469 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0672 - lr: 1.0000e-05 - 202ms/epoch - 5ms/step
Epoch 304/500

Epoch 00304: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0470 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0679 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 305/500

Epoch 00305: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0472 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0673 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 306/500

Epoch 00306: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0461 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0670 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 307/500

Epoch 00307: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0435 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0670 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 308/500

Epoch 00308: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0464 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0673 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 309/500

Epoch 00309: val_loss did not improve from 0.00790
43/43 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0480 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0668 - lr: 1.0000e-05 - 189ms/epoch - 4ms/step
Epoch 310/500

Epoch 00310: val_loss improved from 0.00790 to 0.00778, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0446 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0663 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 311/500

Epoch 00311: val_loss improved from 0.00778 to 0.00778, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0448 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0663 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 312/500

Epoch 00312: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0463 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0667 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 313/500

Epoch 00313: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0455 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0675 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 314/500

Epoch 00314: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0439 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0670 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 315/500

Epoch 00315: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0480 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0675 - lr: 1.0000e-05 - 210ms/epoch - 5ms/step
Epoch 316/500

Epoch 00316: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0450 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0681 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 317/500

Epoch 00317: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0461 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0676 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 318/500

Epoch 00318: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0464 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0678 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 319/500

Epoch 00319: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0462 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0676 - lr: 1.0000e-05 - 178ms/epoch - 4ms/step
Epoch 320/500

Epoch 00320: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0467 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0678 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 321/500

Epoch 00321: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0465 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0684 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 322/500

Epoch 00322: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0465 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0683 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 323/500

Epoch 00323: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0454 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0685 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 324/500

Epoch 00324: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0436 - val_loss: 0.0087 - val_mse: 0.0087 - val_mae: 0.0699 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 325/500

Epoch 00325: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0465 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0690 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 326/500

Epoch 00326: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0476 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0675 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 327/500

Epoch 00327: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0450 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0686 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 328/500

Epoch 00328: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0460 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0684 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 329/500

Epoch 00329: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0479 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0678 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 330/500

Epoch 00330: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0449 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0672 - lr: 1.0000e-05 - 175ms/epoch - 4ms/step
Epoch 331/500

Epoch 00331: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0444 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0667 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 332/500

Epoch 00332: val_loss did not improve from 0.00778
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0665 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 333/500

Epoch 00333: val_loss improved from 0.00778 to 0.00772, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0459 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0656 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 334/500

Epoch 00334: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0487 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0659 - lr: 1.0000e-05 - 215ms/epoch - 5ms/step
Epoch 335/500

Epoch 00335: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0466 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0668 - lr: 1.0000e-05 - 185ms/epoch - 4ms/step
Epoch 336/500

Epoch 00336: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0446 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0671 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 337/500

Epoch 00337: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0470 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0666 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 338/500

Epoch 00338: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0479 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0670 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 339/500

Epoch 00339: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0451 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0671 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 340/500

Epoch 00340: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0672 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 341/500

Epoch 00341: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0442 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0675 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 342/500

Epoch 00342: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0472 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0674 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 343/500

Epoch 00343: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0447 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0661 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 344/500

Epoch 00344: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0461 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0665 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 345/500

Epoch 00345: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0465 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0661 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 346/500

Epoch 00346: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0448 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0659 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 347/500

Epoch 00347: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0474 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0658 - lr: 1.0000e-05 - 184ms/epoch - 4ms/step
Epoch 348/500

Epoch 00348: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0466 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0654 - lr: 1.0000e-05 - 193ms/epoch - 4ms/step
Epoch 349/500

Epoch 00349: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0479 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0655 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 350/500

Epoch 00350: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0455 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0661 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 351/500

Epoch 00351: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0456 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0664 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 352/500

Epoch 00352: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0676 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 353/500

Epoch 00353: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0459 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0669 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 354/500

Epoch 00354: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0465 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0658 - lr: 1.0000e-05 - 257ms/epoch - 6ms/step
Epoch 355/500

Epoch 00355: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0467 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0660 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 356/500

Epoch 00356: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0455 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0660 - lr: 1.0000e-05 - 194ms/epoch - 5ms/step
Epoch 357/500

Epoch 00357: val_loss did not improve from 0.00772
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0452 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0656 - lr: 1.0000e-05 - 205ms/epoch - 5ms/step
Epoch 358/500

Epoch 00358: val_loss improved from 0.00772 to 0.00762, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0443 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0649 - lr: 1.0000e-05 - 239ms/epoch - 6ms/step
Epoch 359/500

Epoch 00359: val_loss did not improve from 0.00762
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0451 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0649 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 360/500

Epoch 00360: val_loss did not improve from 0.00762
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0446 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0658 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 361/500

Epoch 00361: val_loss did not improve from 0.00762
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0439 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0653 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 362/500

Epoch 00362: val_loss did not improve from 0.00762
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0469 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0649 - lr: 1.0000e-05 - 202ms/epoch - 5ms/step
Epoch 363/500

Epoch 00363: val_loss did not improve from 0.00762
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0460 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0658 - lr: 1.0000e-05 - 195ms/epoch - 5ms/step
Epoch 364/500

Epoch 00364: val_loss did not improve from 0.00762
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0660 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 365/500

Epoch 00365: val_loss improved from 0.00762 to 0.00750, saving model to LSTM7.h5
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0428 - val_loss: 0.0075 - val_mse: 0.0075 - val_mae: 0.0643 - lr: 1.0000e-05 - 203ms/epoch - 5ms/step
Epoch 366/500

Epoch 00366: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0449 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0645 - lr: 1.0000e-05 - 169ms/epoch - 4ms/step
Epoch 367/500

Epoch 00367: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0461 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0651 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 368/500

Epoch 00368: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0452 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0645 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 369/500

Epoch 00369: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0654 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 370/500

Epoch 00370: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0432 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0660 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 371/500

Epoch 00371: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0439 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0659 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 372/500

Epoch 00372: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0429 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0649 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 373/500

Epoch 00373: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0477 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0656 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 374/500

Epoch 00374: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0457 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0651 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 375/500

Epoch 00375: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0458 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0661 - lr: 1.0000e-05 - 204ms/epoch - 5ms/step
Epoch 376/500

Epoch 00376: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0443 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0661 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 377/500

Epoch 00377: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0445 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0658 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 378/500

Epoch 00378: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0448 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0655 - lr: 1.0000e-05 - 183ms/epoch - 4ms/step
Epoch 379/500

Epoch 00379: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0645 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 380/500

Epoch 00380: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0451 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0646 - lr: 1.0000e-05 - 180ms/epoch - 4ms/step
Epoch 381/500

Epoch 00381: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0435 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0647 - lr: 1.0000e-05 - 229ms/epoch - 5ms/step
Epoch 382/500

Epoch 00382: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0447 - val_loss: 0.0075 - val_mse: 0.0075 - val_mae: 0.0642 - lr: 1.0000e-05 - 194ms/epoch - 5ms/step
Epoch 383/500

Epoch 00383: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0454 - val_loss: 0.0075 - val_mse: 0.0075 - val_mae: 0.0644 - lr: 1.0000e-05 - 236ms/epoch - 5ms/step
Epoch 384/500

Epoch 00384: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0451 - val_loss: 0.0075 - val_mse: 0.0075 - val_mae: 0.0643 - lr: 1.0000e-05 - 182ms/epoch - 4ms/step
Epoch 385/500

Epoch 00385: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0448 - val_loss: 0.0076 - val_mse: 0.0076 - val_mae: 0.0647 - lr: 1.0000e-05 - 194ms/epoch - 5ms/step
Epoch 386/500

Epoch 00386: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0431 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0655 - lr: 1.0000e-05 - 211ms/epoch - 5ms/step
Epoch 387/500

Epoch 00387: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0445 - val_loss: 0.0079 - val_mse: 0.0079 - val_mae: 0.0659 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 388/500

Epoch 00388: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0430 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0653 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 389/500

Epoch 00389: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0428 - val_loss: 0.0077 - val_mse: 0.0077 - val_mae: 0.0650 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 390/500

Epoch 00390: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0469 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0662 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 391/500

Epoch 00391: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0030 - mse: 0.0030 - mae: 0.0435 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0675 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 392/500

Epoch 00392: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0451 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0680 - lr: 1.0000e-05 - 207ms/epoch - 5ms/step
Epoch 393/500

Epoch 00393: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0035 - mse: 0.0035 - mae: 0.0457 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0682 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 394/500

Epoch 00394: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0443 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0686 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 395/500

Epoch 00395: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0452 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0687 - lr: 1.0000e-05 - 208ms/epoch - 5ms/step
Epoch 396/500

Epoch 00396: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0439 - val_loss: 0.0085 - val_mse: 0.0085 - val_mae: 0.0686 - lr: 1.0000e-05 - 223ms/epoch - 5ms/step
Epoch 397/500

Epoch 00397: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0451 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0673 - lr: 1.0000e-05 - 212ms/epoch - 5ms/step
Epoch 398/500

Epoch 00398: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0446 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0667 - lr: 1.0000e-05 - 179ms/epoch - 4ms/step
Epoch 399/500

Epoch 00399: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0469 - val_loss: 0.0078 - val_mse: 0.0078 - val_mae: 0.0656 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 400/500

Epoch 00400: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0437 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0663 - lr: 1.0000e-05 - 201ms/epoch - 5ms/step
Epoch 401/500

Epoch 00401: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0441 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0680 - lr: 1.0000e-05 - 187ms/epoch - 4ms/step
Epoch 402/500

Epoch 00402: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0440 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0677 - lr: 1.0000e-05 - 243ms/epoch - 6ms/step
Epoch 403/500

Epoch 00403: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0461 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0672 - lr: 1.0000e-05 - 203ms/epoch - 5ms/step
Epoch 404/500

Epoch 00404: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0437 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0661 - lr: 1.0000e-05 - 209ms/epoch - 5ms/step
Epoch 405/500

Epoch 00405: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0435 - val_loss: 0.0080 - val_mse: 0.0080 - val_mae: 0.0662 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 406/500

Epoch 00406: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0438 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0665 - lr: 1.0000e-05 - 230ms/epoch - 5ms/step
Epoch 407/500

Epoch 00407: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0451 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0678 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 408/500

Epoch 00408: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0429 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0690 - lr: 1.0000e-05 - 191ms/epoch - 4ms/step
Epoch 409/500

Epoch 00409: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0033 - mse: 0.0033 - mae: 0.0444 - val_loss: 0.0086 - val_mse: 0.0086 - val_mae: 0.0689 - lr: 1.0000e-05 - 199ms/epoch - 5ms/step
Epoch 410/500

Epoch 00410: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0462 - val_loss: 0.0084 - val_mse: 0.0084 - val_mae: 0.0680 - lr: 1.0000e-05 - 192ms/epoch - 4ms/step
Epoch 411/500

Epoch 00411: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0471 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0673 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 412/500

Epoch 00412: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0031 - mse: 0.0031 - mae: 0.0440 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0675 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 413/500

Epoch 00413: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0029 - mse: 0.0029 - mae: 0.0423 - val_loss: 0.0083 - val_mse: 0.0083 - val_mae: 0.0674 - lr: 1.0000e-05 - 178ms/epoch - 4ms/step
Epoch 414/500

Epoch 00414: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0447 - val_loss: 0.0082 - val_mse: 0.0082 - val_mae: 0.0670 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 415/500

Epoch 00415: val_loss did not improve from 0.00750
43/43 - 0s - loss: 0.0032 - mse: 0.0032 - mae: 0.0434 - val_loss: 0.0081 - val_mse: 0.0081 - val_mae: 0.0668 - lr: 1.0000e-05 - 181ms/epoch - 4ms/step
Epoch 00415: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 42.30488040231578 
RMSE:	 6.504220199402522 
MAPE:	 5.010195929360332

DEMA
Prediction vs Close:		55.6% Accuracy
Prediction vs Prediction:	54.48% Accuracy
MSE:	 23.305922116020078 
RMSE:	 4.827620751055335 
MAPE:	 3.7452201197397774

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 18.082341646298453 
RMSE:	 4.252333670621163 
MAPE:	 3.4333194517527637

MIDPOINT
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 91.59813707600279 
RMSE:	 9.57069156727991 
MAPE:	 7.718313236319782

T3
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 145.19295971499469 
RMSE:	 12.049604131049065 
MAPE:	 9.875885491811884
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16736.686, Time=3.46 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-15327.143, Time=3.48 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15166.078, Time=7.33 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14962.662, Time=14.27 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16731.606, Time=5.59 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14848.952, Time=10.35 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16921.745, Time=6.06 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14958.662, Time=18.28 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15003.046, Time=13.47 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-16752.122, Time=4.06 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 86.368 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8492.873
Date:                Sun, 12 Dec 2021   AIC                         -16921.745
Time:                        19:05:25   BIC                         -16771.638
Sample:                             0   HQIC                        -16864.098
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          2.277e-08      0.001   3.25e-05      1.000      -0.001       0.001
x2          2.286e-08      0.001    2.5e-05      1.000      -0.002       0.002
x3          2.286e-08      0.001   3.44e-05      1.000      -0.001       0.001
x4             1.0000      0.000   3190.279      0.000       0.999       1.001
x5          2.174e-08      0.001   4.21e-05      1.000      -0.001       0.001
x6          6.124e-09   3.05e-05      0.000      1.000   -5.97e-05    5.97e-05
x7          2.246e-08      0.001   1.67e-05      1.000      -0.003       0.003
x8            -0.0013      0.001     -1.669      0.095      -0.003       0.000
x9         -5.239e-09      0.000  -1.79e-05      1.000      -0.001       0.001
x10            0.0001    9.9e-05      1.396      0.163   -5.59e-05       0.000
x11           -0.0001      0.001     -0.177      0.859      -0.002       0.001
x12            0.0012      0.001      1.426      0.154      -0.000       0.003
x13         2.284e-08      0.000   6.75e-05      1.000      -0.001       0.001
x14         6.258e-08      0.001   5.07e-05      1.000      -0.002       0.002
x15         2.215e-08      0.000      0.000      1.000      -0.000       0.000
x16         3.243e-08      0.000      0.000      1.000      -0.001       0.001
x17          2.22e-08      0.000      0.000      1.000      -0.000       0.000
x18         7.527e-09      0.000   1.67e-05      1.000      -0.001       0.001
x19         2.477e-08      0.000      0.000      1.000      -0.000       0.000
x20        -2.348e-08      0.000  -5.78e-05      1.000      -0.001       0.001
x21         2.718e-08    5.8e-05      0.000      1.000      -0.000       0.000
x22        -2.176e-10      0.000  -5.27e-07      1.000      -0.001       0.001
x23         -2.69e-09   8.49e-05  -3.17e-05      1.000      -0.000       0.000
x24        -4.516e-08   7.24e-06     -0.006      0.995   -1.42e-05    1.41e-05
x25        -4.213e-08   2.81e-05     -0.002      0.999   -5.51e-05     5.5e-05
x26         7.946e-08      0.001      0.000      1.000      -0.001       0.001
x27         4.528e-08      0.001   6.21e-05      1.000      -0.001       0.001
x28          5.92e-08      0.001   4.12e-05      1.000      -0.003       0.003
x29         3.468e-08      0.000   7.06e-05      1.000      -0.001       0.001
ma.L1         -1.3739   4.46e-06  -3.08e+05      0.000      -1.374      -1.374
ma.L2          0.3968    1.4e-05   2.84e+04      0.000       0.397       0.397
sigma2      7.701e-11   7.39e-11      1.043      0.297   -6.78e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  61.47   Jarque-Bera (JB):           5565463.09
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                            10.97
Prob(H) (two-sided):                  0.00   Kurtosis:                       409.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 6.67e+20. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.03716, saving model to LSTM7.h5
90/90 - 3s - loss: 0.0617 - mse: 0.0617 - mae: 0.2014 - val_loss: 0.0372 - val_mse: 0.0372 - val_mae: 0.1565 - lr: 0.0010 - 3s/epoch - 29ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.03716
90/90 - 0s - loss: 0.0580 - mse: 0.0580 - mae: 0.2030 - val_loss: 0.0943 - val_mse: 0.0943 - val_mae: 0.2265 - lr: 0.0010 - 374ms/epoch - 4ms/step
Epoch 3/500

Epoch 00003: val_loss improved from 0.03716 to 0.03535, saving model to LSTM7.h5
90/90 - 0s - loss: 0.0561 - mse: 0.0561 - mae: 0.1766 - val_loss: 0.0353 - val_mse: 0.0353 - val_mae: 0.1545 - lr: 0.0010 - 387ms/epoch - 4ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.03535
90/90 - 0s - loss: 0.0112 - mse: 0.0112 - mae: 0.0810 - val_loss: 0.0895 - val_mse: 0.0895 - val_mae: 0.2354 - lr: 0.0010 - 353ms/epoch - 4ms/step
Epoch 5/500

Epoch 00005: val_loss improved from 0.03535 to 0.02650, saving model to LSTM7.h5
90/90 - 0s - loss: 0.0121 - mse: 0.0121 - mae: 0.0824 - val_loss: 0.0265 - val_mse: 0.0265 - val_mae: 0.1325 - lr: 0.0010 - 384ms/epoch - 4ms/step
Epoch 6/500

Epoch 00006: val_loss did not improve from 0.02650
90/90 - 0s - loss: 0.0114 - mse: 0.0114 - mae: 0.0815 - val_loss: 0.0968 - val_mse: 0.0968 - val_mae: 0.2570 - lr: 0.0010 - 387ms/epoch - 4ms/step
Epoch 7/500

Epoch 00007: val_loss improved from 0.02650 to 0.02480, saving model to LSTM7.h5
90/90 - 0s - loss: 0.0096 - mse: 0.0096 - mae: 0.0742 - val_loss: 0.0248 - val_mse: 0.0248 - val_mae: 0.1259 - lr: 0.0010 - 383ms/epoch - 4ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0084 - mse: 0.0084 - mae: 0.0713 - val_loss: 0.1028 - val_mse: 0.1028 - val_mae: 0.2678 - lr: 0.0010 - 461ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0076 - mse: 0.0076 - mae: 0.0661 - val_loss: 0.0284 - val_mse: 0.0284 - val_mae: 0.1277 - lr: 0.0010 - 375ms/epoch - 4ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0074 - mse: 0.0074 - mae: 0.0653 - val_loss: 0.0942 - val_mse: 0.0942 - val_mae: 0.2562 - lr: 0.0010 - 365ms/epoch - 4ms/step
Epoch 11/500

Epoch 00011: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0078 - mse: 0.0078 - mae: 0.0677 - val_loss: 0.0346 - val_mse: 0.0346 - val_mae: 0.1364 - lr: 0.0010 - 465ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00012: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0068 - mse: 0.0068 - mae: 0.0622 - val_loss: 0.1167 - val_mse: 0.1167 - val_mae: 0.2958 - lr: 0.0010 - 372ms/epoch - 4ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0127 - mse: 0.0127 - mae: 0.0938 - val_loss: 0.0828 - val_mse: 0.0828 - val_mae: 0.2357 - lr: 1.0000e-04 - 366ms/epoch - 4ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0056 - mse: 0.0056 - mae: 0.0590 - val_loss: 0.0758 - val_mse: 0.0758 - val_mae: 0.2222 - lr: 1.0000e-04 - 470ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0052 - mse: 0.0052 - mae: 0.0578 - val_loss: 0.0708 - val_mse: 0.0708 - val_mae: 0.2123 - lr: 1.0000e-04 - 382ms/epoch - 4ms/step
Epoch 16/500

Epoch 00016: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0050 - mse: 0.0050 - mae: 0.0549 - val_loss: 0.0687 - val_mse: 0.0687 - val_mae: 0.2080 - lr: 1.0000e-04 - 374ms/epoch - 4ms/step
Epoch 17/500

Epoch 00017: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00017: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0051 - mse: 0.0051 - mae: 0.0556 - val_loss: 0.0653 - val_mse: 0.0653 - val_mae: 0.2011 - lr: 1.0000e-04 - 432ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0530 - val_loss: 0.0652 - val_mse: 0.0652 - val_mae: 0.2010 - lr: 1.0000e-05 - 366ms/epoch - 4ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0047 - mse: 0.0047 - mae: 0.0530 - val_loss: 0.0653 - val_mse: 0.0653 - val_mae: 0.2013 - lr: 1.0000e-05 - 371ms/epoch - 4ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0504 - val_loss: 0.0657 - val_mse: 0.0657 - val_mae: 0.2020 - lr: 1.0000e-05 - 369ms/epoch - 4ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0507 - val_loss: 0.0656 - val_mse: 0.0656 - val_mae: 0.2019 - lr: 1.0000e-05 - 368ms/epoch - 4ms/step
Epoch 22/500

Epoch 00022: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00022: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0503 - val_loss: 0.0658 - val_mse: 0.0658 - val_mae: 0.2024 - lr: 1.0000e-05 - 372ms/epoch - 4ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0489 - val_loss: 0.0660 - val_mse: 0.0660 - val_mae: 0.2029 - lr: 1.0000e-05 - 413ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0524 - val_loss: 0.0661 - val_mse: 0.0661 - val_mae: 0.2031 - lr: 1.0000e-05 - 402ms/epoch - 4ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0503 - val_loss: 0.0660 - val_mse: 0.0660 - val_mae: 0.2031 - lr: 1.0000e-05 - 458ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0500 - val_loss: 0.0660 - val_mse: 0.0660 - val_mae: 0.2031 - lr: 1.0000e-05 - 385ms/epoch - 4ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0515 - val_loss: 0.0660 - val_mse: 0.0660 - val_mae: 0.2031 - lr: 1.0000e-05 - 369ms/epoch - 4ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0044 - mse: 0.0044 - mae: 0.0513 - val_loss: 0.0659 - val_mse: 0.0659 - val_mae: 0.2030 - lr: 1.0000e-05 - 365ms/epoch - 4ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0499 - val_loss: 0.0655 - val_mse: 0.0655 - val_mae: 0.2022 - lr: 1.0000e-05 - 377ms/epoch - 4ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0491 - val_loss: 0.0653 - val_mse: 0.0653 - val_mae: 0.2020 - lr: 1.0000e-05 - 376ms/epoch - 4ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0516 - val_loss: 0.0654 - val_mse: 0.0654 - val_mae: 0.2021 - lr: 1.0000e-05 - 386ms/epoch - 4ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0043 - mse: 0.0043 - mae: 0.0513 - val_loss: 0.0649 - val_mse: 0.0649 - val_mae: 0.2011 - lr: 1.0000e-05 - 362ms/epoch - 4ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0045 - mse: 0.0045 - mae: 0.0520 - val_loss: 0.0644 - val_mse: 0.0644 - val_mae: 0.2003 - lr: 1.0000e-05 - 389ms/epoch - 4ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0506 - val_loss: 0.0638 - val_mse: 0.0638 - val_mae: 0.1990 - lr: 1.0000e-05 - 397ms/epoch - 4ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0042 - mse: 0.0042 - mae: 0.0519 - val_loss: 0.0638 - val_mse: 0.0638 - val_mae: 0.1990 - lr: 1.0000e-05 - 408ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0484 - val_loss: 0.0638 - val_mse: 0.0638 - val_mae: 0.1991 - lr: 1.0000e-05 - 412ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0495 - val_loss: 0.0640 - val_mse: 0.0640 - val_mae: 0.1995 - lr: 1.0000e-05 - 359ms/epoch - 4ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0475 - val_loss: 0.0640 - val_mse: 0.0640 - val_mae: 0.1995 - lr: 1.0000e-05 - 380ms/epoch - 4ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0489 - val_loss: 0.0642 - val_mse: 0.0642 - val_mae: 0.1999 - lr: 1.0000e-05 - 381ms/epoch - 4ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0481 - val_loss: 0.0642 - val_mse: 0.0642 - val_mae: 0.2001 - lr: 1.0000e-05 - 371ms/epoch - 4ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0479 - val_loss: 0.0642 - val_mse: 0.0642 - val_mae: 0.2001 - lr: 1.0000e-05 - 380ms/epoch - 4ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0487 - val_loss: 0.0637 - val_mse: 0.0637 - val_mae: 0.1991 - lr: 1.0000e-05 - 366ms/epoch - 4ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0470 - val_loss: 0.0636 - val_mse: 0.0636 - val_mae: 0.1991 - lr: 1.0000e-05 - 366ms/epoch - 4ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0465 - val_loss: 0.0635 - val_mse: 0.0635 - val_mae: 0.1987 - lr: 1.0000e-05 - 410ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0040 - mse: 0.0040 - mae: 0.0487 - val_loss: 0.0634 - val_mse: 0.0634 - val_mae: 0.1986 - lr: 1.0000e-05 - 368ms/epoch - 4ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0480 - val_loss: 0.0634 - val_mse: 0.0634 - val_mae: 0.1986 - lr: 1.0000e-05 - 432ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0041 - mse: 0.0041 - mae: 0.0493 - val_loss: 0.0634 - val_mse: 0.0634 - val_mae: 0.1986 - lr: 1.0000e-05 - 399ms/epoch - 4ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0037 - mse: 0.0037 - mae: 0.0477 - val_loss: 0.0632 - val_mse: 0.0632 - val_mae: 0.1982 - lr: 1.0000e-05 - 374ms/epoch - 4ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.02480
90/90 - 1s - loss: 0.0042 - mse: 0.0042 - mae: 0.0501 - val_loss: 0.0631 - val_mse: 0.0631 - val_mae: 0.1981 - lr: 1.0000e-05 - 504ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0491 - val_loss: 0.0633 - val_mse: 0.0633 - val_mae: 0.1985 - lr: 1.0000e-05 - 366ms/epoch - 4ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0458 - val_loss: 0.0633 - val_mse: 0.0633 - val_mae: 0.1986 - lr: 1.0000e-05 - 459ms/epoch - 5ms/step
Epoch 52/500

Epoch 00052: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0477 - val_loss: 0.0633 - val_mse: 0.0633 - val_mae: 0.1988 - lr: 1.0000e-05 - 369ms/epoch - 4ms/step
Epoch 53/500

Epoch 00053: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0454 - val_loss: 0.0633 - val_mse: 0.0633 - val_mae: 0.1986 - lr: 1.0000e-05 - 418ms/epoch - 5ms/step
Epoch 54/500

Epoch 00054: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0034 - mse: 0.0034 - mae: 0.0454 - val_loss: 0.0632 - val_mse: 0.0632 - val_mae: 0.1985 - lr: 1.0000e-05 - 382ms/epoch - 4ms/step
Epoch 55/500

Epoch 00055: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0039 - mse: 0.0039 - mae: 0.0489 - val_loss: 0.0636 - val_mse: 0.0636 - val_mae: 0.1994 - lr: 1.0000e-05 - 456ms/epoch - 5ms/step
Epoch 56/500

Epoch 00056: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0036 - mse: 0.0036 - mae: 0.0462 - val_loss: 0.0636 - val_mse: 0.0636 - val_mae: 0.1995 - lr: 1.0000e-05 - 475ms/epoch - 5ms/step
Epoch 57/500

Epoch 00057: val_loss did not improve from 0.02480
90/90 - 0s - loss: 0.0038 - mse: 0.0038 - mae: 0.0475 - val_loss: 0.0642 - val_mse: 0.0642 - val_mae: 0.2007 - lr: 1.0000e-05 - 365ms/epoch - 4ms/step
Epoch 00057: early stopping
SMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 44.65212926265077 
RMSE:	 6.682224873696692 
MAPE:	 5.204686480071648

EMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 45.539825469272486 
RMSE:	 6.748320196113436 
MAPE:	 5.43245952292463

WMA
Prediction vs Close:		52.99% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 42.30488040231578 
RMSE:	 6.504220199402522 
MAPE:	 5.010195929360332

DEMA
Prediction vs Close:		55.6% Accuracy
Prediction vs Prediction:	54.48% Accuracy
MSE:	 23.305922116020078 
RMSE:	 4.827620751055335 
MAPE:	 3.7452201197397774

KAMA
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 18.082341646298453 
RMSE:	 4.252333670621163 
MAPE:	 3.4333194517527637

MIDPOINT
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	50.0% Accuracy
MSE:	 91.59813707600279 
RMSE:	 9.57069156727991 
MAPE:	 7.718313236319782

T3
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 145.19295971499469 
RMSE:	 12.049604131049065 
MAPE:	 9.875885491811884

TEMA
Prediction vs Close:		51.12% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 41.1158513706741 
RMSE:	 6.412164328109044 
MAPE:	 5.720374187090847
Runtime: mins: 60.462800946899975

Architecture Used

In [130]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving Experiment7.png to Experiment7 (1).png
In [139]:
img = cv2.imread('Experiment7.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[139]:
<matplotlib.image.AxesImage at 0x7fa65007efd0>

Model Plots

In [132]:
for i in range(len(list(simulation7.keys()))):
  SIM = list(simulation7.keys())[i]
  plot_train(simulation7,SIM)
  plot_test(simulation7,SIM)
----- Train RMSE for SMA ----- 9.009338337956548
----- Train_MSE_LSTM for SMA ----- 81.16817728777366
----- Train MAE LSTM for SMA ----- 7.791349936760778
----- Test RMSE for SMA----- 6.682224873696692
----- Test_MSE_LSTM for SMA----- 44.65212926265077
----- Test_MAE_LSTM for SMA----- 5.204686480071648
----- Train RMSE for EMA ----- 10.75589930055345
----- Train_MSE_LSTM for EMA ----- 115.68936976364621
----- Train MAE LSTM for EMA ----- 9.582493831136144
----- Test RMSE for EMA----- 6.748320196113436
----- Test_MSE_LSTM for EMA----- 45.539825469272486
----- Test_MAE_LSTM for EMA----- 5.43245952292463
----- Train RMSE for WMA ----- 10.908509232790552
----- Train_MSE_LSTM for WMA ----- 118.9955736818767
----- Train MAE LSTM for WMA ----- 9.800814582394565
----- Test RMSE for WMA----- 6.504220199402522
----- Test_MSE_LSTM for WMA----- 42.30488040231578
----- Test_MAE_LSTM for WMA----- 5.010195929360332
----- Train RMSE for DEMA ----- 12.643440847440496
----- Train_MSE_LSTM for DEMA ----- 159.85659646272686
----- Train MAE LSTM for DEMA ----- 11.36962745253845
----- Test RMSE for DEMA----- 4.827620751055335
----- Test_MSE_LSTM for DEMA----- 23.305922116020078
----- Test_MAE_LSTM for DEMA----- 3.7452201197397774
----- Train RMSE for KAMA ----- 11.147835109393704
----- Train_MSE_LSTM for KAMA ----- 124.27422762623091
----- Train MAE LSTM for KAMA ----- 10.178621601225753
----- Test RMSE for KAMA----- 4.252333670621163
----- Test_MSE_LSTM for KAMA----- 18.082341646298453
----- Test_MAE_LSTM for KAMA----- 3.4333194517527637
----- Train RMSE for MIDPOINT ----- 9.563103791664194
----- Train_MSE_LSTM for MIDPOINT ----- 91.45295413014207
----- Train MAE LSTM for MIDPOINT ----- 8.50176313593246
----- Test RMSE for MIDPOINT----- 9.57069156727991
----- Test_MSE_LSTM for MIDPOINT----- 91.59813707600279
----- Test_MAE_LSTM for MIDPOINT----- 7.718313236319782
----- Train RMSE for T3 ----- 12.352918439245476
----- Train_MSE_LSTM for T3 ----- 152.5945939666509
----- Train MAE LSTM for T3 ----- 11.209855832067309
----- Test RMSE for T3----- 12.049604131049065
----- Test_MSE_LSTM for T3----- 145.19295971499469
----- Test_MAE_LSTM for T3----- 9.875885491811884
----- Train RMSE for TEMA ----- 7.436400272753141
----- Train_MSE_LSTM for TEMA ----- 55.30004901660298
----- Train MAE LSTM for TEMA ----- 5.158390050894572
----- Test RMSE for TEMA----- 6.412164328109044
----- Test_MSE_LSTM for TEMA----- 41.1158513706741
----- Test_MAE_LSTM for TEMA----- 5.720374187090847

Arima w Exogenous Variable Multistep MutiVariate LSTM Hybrid Model Experiment 8

In [133]:
def get_arima_exog(dataframe,original_data, train_len, test_len):    
    

    # prepare train and test data for exogenous vr
    X_value = pd.DataFrame(low_vol.iloc[:, :])
    y_value = pd.DataFrame(low_vol.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    X_scale_dataset = X_scaler.fit_transform(X_value)
    y_scale_dataset = y_scaler.fit_transform(y_value)
    # Get data and check shape
    # X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X_scale_dataset)
    y_train, y_test, = split_train_test(y_scale_dataset)
    yc_train,yc_test = split_train_test(low_vol_data)
    yc = yc_test.values.tolist()
    y_train_list = y_train.flatten().tolist()
    y_test_list = y_test.flatten().tolist()
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)

    # Initialize model
    model = auto_arima(y_train_list,exogenous  = X_train,trace=True, error_action='ignore', start_p=1,start_q=1,max_p=3,max_q=3,d=3,
            suppress_warnings=True,stepwise=True,seasonal=True)

      # Determine model parameters
    print(model.summary())
    model.fit(y_train_list,maxiter=200)
    order = model.get_params()['order']
    print('ARIMA order:', order, '\n')

      # Genereate predictions
    prediction = []
    for i in range(len(y_test_list)):
        model = pmdarima.ARIMA(order=order)
        model.fit(y_train_list)
        # print('working on', i+1, 'of', len(y_test), '-- ' + str(int(100 * (i + 1) / len(y_test))) + '% complete')

        prediction.append(model.predict()[0])
        y_train_list.append(y_test_list[i])

    predictionte = y_scaler.inverse_transform(np.array(prediction).reshape(-1,1))
    y_test_ = y_scaler.inverse_transform(np.array(y_test_list).reshape(-1,1))

    # Generate error data
    mse = mean_squared_error(yc_test, predictionte)
    rmse = mse ** 0.5
    mae = mean_absolute_error(y_test_ , predictionte )
    return yc,predictionte.flatten().tolist(), mse, rmse, mae
In [134]:
def get_lstm(data,original_data, train_len, test_len,img_file,ma ,lstm_len=3):
    # prepare train and test data
    X_value = pd.DataFrame(data.iloc[:, :])
    y_value = pd.DataFrame(data.iloc[:, 3])
    X_scaler = MinMaxScaler(feature_range=(-1, 1))
    y_scaler = MinMaxScaler(feature_range=(-1, 1))
    X_scaler.fit(X_value)
    y_scaler.fit(y_value)
    # Get data and check shape
    X, y, yc = get_X_y(X_scale_dataset, y_scale_dataset)#X will be of shape 224 X 3 X 21 (each 3 X 21 array will be 3 days' worth of data). yc will have the corresponding closing price value
    # pdb.set_trace()
    X_train, X_test, = split_train_test(X)
    y_train, y_test, = split_train_test(y)
    # yc_train, yc_test, = split_train_test(original_data)
    index_train, index_test, = predict_index(dataset_final, X_train, n_steps_in, n_steps_out)
    det =20
    input_dim = X_train.shape[1]#3
    feature_size = X_train.shape[2]#24
    output_dim = y_train.shape[1]#1



    # Option 1
    # Set up & fit LSTM RNN
    # model = Sequential()
    # model.add(LSTM(256, activation='relu', kernel_initializer='he_normal', input_shape=(input_dim, feature_size)))
    # model.add(Dense(units=64,activation='relu'))
    # model.add(Dropout(0.5))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(learning_rate = 0.001), loss='mse')

    # ## Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM1.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma_' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()


    # # # option 2
    # model = Sequential()
    # model.add(Bidirectional(LSTM(units= 128), input_shape=(input_dim, feature_size)))
    # model.add(Dense(64))
    # model.add(Dense(units=output_dim))
    # model.compile(optimizer=Adam(lr = 0.001), loss='mean_squared_error', metrics=['accuracy'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM7.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma+' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # Option 3
    # define custom activation
    # reference: https://github.com/Vaibhav-Sachdeva/Correlation-Coefficient-Prediction-using-ARIMA-LSTM-Hybrid-Model/blob/main/Code/LSTM-ARIMA.ipynb
    # class Double_Tanh(Activation):
    #     def __init__(self, activation, **kwargs):
    #         super(Double_Tanh, self).__init__(activation, **kwargs)
    #         self.__name__ = 'double_tanh'

    # def double_tanh(x):
    #     return (K.tanh(x) * 2)

    # get_custom_objects().update({'double_tanh':Double_Tanh(double_tanh)})
    #     # Model Generation
    # model = Sequential()
    # #check https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
    # model.add(LSTM(25, input_shape=(input_dim, feature_size), dropout=0.2, kernel_regularizer=l1_l2(0.00,0.00), bias_regularizer=l1_l2(0.00,0.00)))
    # model.add(Dense(1))
    # model.add(Activation(double_tanh))
    # model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', 'mae'])
    # # Common code
    # callbacks = [
    # EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    # ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    # ModelCheckpoint('LSTM7.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    # fname1 = img_file+'.png'
    # tensorflow.keras.utils.plot_model(
    #     model, to_file=fname1, show_shapes=True, show_dtype=False,
    #     show_layer_names=True, expand_nested=False, dpi=96,
    #     layer_range=None, show_layer_activations=False
    # )
    # history = model.fit(X_train, y_train, epochs=500, batch_size=1, verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # # plot loss
    # fname2 = img_file+'-'+ma
    # plt.title(img_file+'-'+ma+' Loss')
    # plt.xlabel("Epochs")
    # plt.ylabel("Loss")
    # pyplot.plot(history.history['loss'], label='train')
    # pyplot.plot(history.history['val_loss'], label='validation')
    # pyplot.legend()
    # pyplot.savefig(fname2+'.png',dpi='figure')
    # pyplot.show()

    # #Option 4
    # # Set up & fit LSTM RNN
    model = Sequential()
    model.add(LSTM(units=lstm_len, return_sequences=True, input_shape=(input_dim, feature_size)))
    model.add(LSTM(units=int(lstm_len/2)))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # Common code
    callbacks = [
    EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=50),
    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),
    ModelCheckpoint('LSTM8.h5', verbose=1, save_best_only=True, save_weights_only=True)]
    fname1 = img_file+'.png'
    tensorflow.keras.utils.plot_model(
        model, to_file=fname1, show_shapes=True, show_dtype=False,
        show_layer_names=True, expand_nested=False, dpi=96,
        layer_range=None, show_layer_activations=False
    )
    history = model.fit(X_train, y_train, epochs=500, batch_size=int( optimized_period[ma]), verbose=2, callbacks=callbacks, validation_data=(X_test, y_test),shuffle=False)
    # plot loss
    fname2 = img_file+'-'+ma
    plt.title(img_file+'-'+ma+' Loss')
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='validation')
    pyplot.legend()
    pyplot.savefig(fname2+'.png',dpi='figure')
    pyplot.show()



    # Generate predictions
    predictiontr = model.predict(X_train, verbose=0)
    predictiontr = y_scaler.inverse_transform(predictiontr).tolist()
    outputtr = []
    for i in range(len(predictiontr)):
        outputtr.extend(predictiontr[i])
    predictiontr = outputtr
    # Generate error data

    ## replace with yc , xtest generated by new multistep method
    mse_tr = mean_squared_error(y_train, predictiontr)
    rmse_tr = mse_tr ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictiontr))
    mae_tr = mean_absolute_error(y_train, pd.Series(predictiontr))
    # Original_tr = pd.Series(yc_train)
    Original_tr = y_scaler.inverse_transform(y_train).flatten().tolist()


    predictionte = model.predict(X_test, verbose=0)
    predictionte = (y_scaler.inverse_transform(predictionte)-det).tolist()
    outputte = []
    for i in range(len(predictionte)):
        outputte.extend(predictionte[i])
    predictionte = outputte
    # Generate error data

    mse_te = mean_squared_error(y_test, predictionte)
    rmse_te = mse_te ** 0.5
    # mape = mean_absolute_percentage_error(X_test, pd.Series(predictionte))
    mae_te = mean_absolute_error(y_test, pd.Series(predictionte))
    # Original_te = pd.Series(yc_test)
    Original_te = y_scaler.inverse_transform(y_test).flatten().tolist()

    return Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,Original_te,predictionte, mse_te,rmse_te,mae_te
In [135]:
if __name__ == '__main__':
    start_time = timeit.default_timer()
    simulation8 = {}
    imgfile = 'Experiment8'
    for ma in optimized_period:
                print(ma)
                print(functions[ma])
                print ( int( optimized_period[ma]))
              # if ma == 'SMA':
                low_vol = df.apply(lambda c:  functions[ma](c, timeperiod = int( optimized_period[ma])))
                low_vol = low_vol.fillna(0)
                low_vol_data = df['close']
                high_vol = pd.DataFrame()
                df2 = df.copy()
                for i in df2.columns:
                  if i in low_vol.columns:
                    high_vol[i] = df2[i].subtract(low_vol[i], fill_value=0)
                high_vol_data = df['close']
                ## *****************************************************
                # Generate ARIMA and LSTM predictions
                print('\nWorking on ' + ma + ' predictions')
                try:
                  print('parameters used : ', train_len, test_len)
                  low_vol_Original, low_vol_prediction, low_vol_mse, low_vol_rmse,low_vol_mae = get_arima_exog(low_vol,low_vol_data, train_len, test_len)
                except:
                    print('ARIMA error, skipping to next MA type')
                    continue
                Original_tr, predictiontr, mse_tr, rmse_tr,mae_tr,high_vol_Original, high_vol_prediction, high_vol_mse, high_vol_rmse,high_vol_mae, = get_lstm(high_vol,high_vol_data, train_len, test_len,imgfile,ma)
                final_prediction_tr = df['close'].head(train_len).values + pd.Series(predictiontr) # ignoring first 3 steps 
                mse_ftr = mean_squared_error(df['close'].head(train_len).values,final_prediction_tr.values)
                rmse_ftr = mse_ftr ** 0.5
                mape_ftr = mean_absolute_percentage_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)
                mae_ftr = mean_absolute_error(df['close'].head(train_len).reset_index(drop=True), final_prediction_tr)

                final_prediction = pd.Series(low_vol_prediction[3:]) + pd.Series(high_vol_prediction)
                mse = mean_squared_error(df['close'].tail(test_len).values,final_prediction.values)
                rmse = mse ** 0.5
                mape = mean_absolute_percentage_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                mae = mean_absolute_error(df['close'].tail(test_len).reset_index(drop=True), final_prediction)
                # Generate prediction accuracy
                actual = df['close'].tail(test_len).values
                result_1 = []
                result_2 = []
                for i in range(1, len(final_prediction)):
                    # Compare prediction to previous close price
                    if final_prediction[i] > actual[i-1] and actual[i] > actual[i-1]:
                        result_1.append(1)
                    elif final_prediction[i] < actual[i-1] and actual[i] < actual[i-1]:
                        result_1.append(1)
                    else:
                        result_1.append(0)

                    # Compare prediction to previous prediction
                    if final_prediction[i] > final_prediction[i-1] and actual[i] > actual[i-1]:
                        result_2.append(1)
                    elif final_prediction[i] < final_prediction[i-1] and actual[i] < actual[i-1]:
                        result_2.append(1)
                    else:
                        result_2.append(0)

                accuracy_1 = np.mean(result_1)
                accuracy_2 = np.mean(result_2)

                simulation8[ma] = {'low_vol': {'original':list(low_vol_Original), 'prediction': list(low_vol_prediction) , 'mse': low_vol_mse,
                                              'rmse': low_vol_rmse, 'mae' : low_vol_mae},
                                  'high_vol': {'original':list(high_vol_Original),'prediction': list(high_vol_prediction), 'mse': high_vol_mse,
                                              'rmse': high_vol_rmse, 'mae' : high_vol_mae},
                                  'final_tr': {'original':df['close'].head(train_len).tolist(),'prediction': final_prediction_tr.values.tolist(), 'mse': mse_ftr,
                                              'rmse': rmse_ftr, 'mae' : mae_ftr},
                                  'final': {'original': df['close'].tail(test_len).tolist(), 'prediction': final_prediction.values.tolist(), 'mse': mse,
                                            'rmse': rmse, 'mae': mae },
                                  'accuracy': {'prediction vs close': accuracy_1, 'prediction vs prediction': accuracy_2}}

                # save simulation data here as checkpoint
                with open('simulation8_data.json', 'w') as fp:
                    json.dump(simulation8, fp)

                for ma in simulation8.keys():
                    print('\n' + ma)
                    print('Prediction vs Close:\t\t' + str(round(100*simulation8[ma]['accuracy']['prediction vs close'], 2))
                          + '% Accuracy')
                    print('Prediction vs Prediction:\t' + str(round(100*simulation8[ma]['accuracy']['prediction vs prediction'], 2))
                          + '% Accuracy')
                    print('MSE:\t', simulation8[ma]['final']['mse'],
                          '\nRMSE:\t', simulation8[ma]['final']['rmse'],
                          '\nMAPE:\t', simulation8[ma]['final']['mae'])#,
                          # '\nMAPE:\t', simulation[ma]['final']['mape'])
              # else:
              #   break
    elapsed = timeit.default_timer() - start_time
    print('Runtime: mins:',elapsed/60)
SMA
SMA([input_arrays], [timeperiod=30])

Simple Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
17

Working on SMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.787, Time=3.71 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.588, Time=5.64 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14596.280, Time=5.73 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.588, Time=8.68 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16924.805, Time=10.33 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14482.349, Time=11.41 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17215.608, Time=21.10 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.588, Time=10.18 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15570.350, Time=18.92 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11671.292, Time=28.02 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 123.736 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8639.804
Date:                Sun, 12 Dec 2021   AIC                         -17215.608
Time:                        19:15:57   BIC                         -17065.501
Sample:                             0   HQIC                        -17157.961
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -4.057e-09   5.82e-05  -6.97e-05      1.000      -0.000       0.000
x2         -4.057e-09   5.81e-05  -6.99e-05      1.000      -0.000       0.000
x3         -4.111e-09   5.49e-05  -7.49e-05      1.000      -0.000       0.000
x4             1.0000   5.71e-05   1.75e+04      0.000       1.000       1.000
x5         -3.706e-09   5.43e-05  -6.82e-05      1.000      -0.000       0.000
x6         -1.082e-08      0.000  -6.08e-05      1.000      -0.000       0.000
x7         -4.025e-09   5.63e-05  -7.15e-05      1.000      -0.000       0.000
x8         -4.035e-09   5.19e-05  -7.78e-05      1.000      -0.000       0.000
x9         -1.522e-10    2.9e-05  -5.25e-06      1.000   -5.68e-05    5.68e-05
x10        -6.396e-10   1.04e-05  -6.15e-05      1.000   -2.04e-05    2.04e-05
x11        -3.921e-09   5.06e-05  -7.75e-05      1.000   -9.91e-05    9.91e-05
x12        -4.102e-09   5.29e-05  -7.76e-05      1.000      -0.000       0.000
x13        -4.087e-09   5.75e-05  -7.11e-05      1.000      -0.000       0.000
x14        -3.619e-08      0.000     -0.000      1.000      -0.000       0.000
x15        -4.806e-09   4.61e-05     -0.000      1.000   -9.03e-05    9.03e-05
x16        -3.507e-09      0.000  -2.98e-05      1.000      -0.000       0.000
x17        -3.121e-09   6.02e-05  -5.18e-05      1.000      -0.000       0.000
x18        -1.172e-08      0.000     -0.000      1.000      -0.000       0.000
x19        -5.433e-09   6.06e-05  -8.96e-05      1.000      -0.000       0.000
x20        -1.393e-08   4.79e-05     -0.000      1.000   -9.39e-05    9.39e-05
x21        -4.216e-09   6.63e-05  -6.36e-05      1.000      -0.000       0.000
x22        -3.479e-11   1.66e-08     -0.002      0.998   -3.25e-08    3.24e-08
x23        -9.221e-10    1.4e-07     -0.007      0.995   -2.74e-07    2.73e-07
x24        -8.085e-08      0.001  -6.96e-05      1.000      -0.002       0.002
x25        -9.642e-08      0.001     -0.000      1.000      -0.002       0.002
x26        -5.019e-08      0.000     -0.000      1.000      -0.000       0.000
x27        -2.457e-08   7.65e-05     -0.000      1.000      -0.000       0.000
x28        -3.411e-08      0.000     -0.000      1.000      -0.000       0.000
x29        -1.507e-08   4.36e-05     -0.000      1.000   -8.54e-05    8.54e-05
ma.L1         -1.3898   8.03e-07  -1.73e+06      0.000      -1.390      -1.390
ma.L2          0.4031   8.36e-07   4.82e+05      0.000       0.403       0.403
sigma2      7.528e-11   7.24e-11      1.040      0.298   -6.66e-11    2.17e-10
===================================================================================
Ljung-Box (L1) (Q):                  89.12   Jarque-Bera (JB):           1533103.33
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                             5.56
Prob(H) (two-sided):                  0.00   Kurtosis:                       216.50
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.08e+25. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04793, saving model to LSTM8.h5
48/48 - 3s - loss: 1.4063 - val_loss: 0.0479 - lr: 0.0010 - 3s/epoch - 71ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04793
48/48 - 0s - loss: 1.1570 - val_loss: 0.0499 - lr: 0.0010 - 233ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.9394 - val_loss: 0.0520 - lr: 0.0010 - 273ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.8556 - val_loss: 0.0541 - lr: 0.0010 - 243ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.8087 - val_loss: 0.0562 - lr: 0.0010 - 267ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7767 - val_loss: 0.0583 - lr: 0.0010 - 262ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7614 - val_loss: 0.0585 - lr: 1.0000e-04 - 258ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7592 - val_loss: 0.0588 - lr: 1.0000e-04 - 234ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7571 - val_loss: 0.0590 - lr: 1.0000e-04 - 266ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7549 - val_loss: 0.0592 - lr: 1.0000e-04 - 277ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7528 - val_loss: 0.0595 - lr: 1.0000e-04 - 258ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7514 - val_loss: 0.0595 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7512 - val_loss: 0.0595 - lr: 1.0000e-05 - 287ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7510 - val_loss: 0.0596 - lr: 1.0000e-05 - 257ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7508 - val_loss: 0.0596 - lr: 1.0000e-05 - 268ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7505 - val_loss: 0.0596 - lr: 1.0000e-05 - 238ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7503 - val_loss: 0.0597 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7501 - val_loss: 0.0597 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7498 - val_loss: 0.0597 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7496 - val_loss: 0.0597 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7494 - val_loss: 0.0598 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7491 - val_loss: 0.0598 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7489 - val_loss: 0.0598 - lr: 1.0000e-05 - 259ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7486 - val_loss: 0.0599 - lr: 1.0000e-05 - 277ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7484 - val_loss: 0.0599 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7481 - val_loss: 0.0599 - lr: 1.0000e-05 - 267ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7479 - val_loss: 0.0600 - lr: 1.0000e-05 - 253ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7477 - val_loss: 0.0600 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7474 - val_loss: 0.0600 - lr: 1.0000e-05 - 259ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7472 - val_loss: 0.0601 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7469 - val_loss: 0.0601 - lr: 1.0000e-05 - 233ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7466 - val_loss: 0.0601 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7464 - val_loss: 0.0602 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7461 - val_loss: 0.0602 - lr: 1.0000e-05 - 266ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7459 - val_loss: 0.0602 - lr: 1.0000e-05 - 272ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7456 - val_loss: 0.0603 - lr: 1.0000e-05 - 255ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7454 - val_loss: 0.0603 - lr: 1.0000e-05 - 256ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7451 - val_loss: 0.0604 - lr: 1.0000e-05 - 288ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7449 - val_loss: 0.0604 - lr: 1.0000e-05 - 258ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7446 - val_loss: 0.0604 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7443 - val_loss: 0.0605 - lr: 1.0000e-05 - 323ms/epoch - 7ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7441 - val_loss: 0.0605 - lr: 1.0000e-05 - 252ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7438 - val_loss: 0.0605 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7436 - val_loss: 0.0606 - lr: 1.0000e-05 - 273ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7433 - val_loss: 0.0606 - lr: 1.0000e-05 - 238ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7431 - val_loss: 0.0607 - lr: 1.0000e-05 - 274ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7428 - val_loss: 0.0607 - lr: 1.0000e-05 - 262ms/epoch - 5ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7425 - val_loss: 0.0607 - lr: 1.0000e-05 - 296ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7423 - val_loss: 0.0608 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7420 - val_loss: 0.0608 - lr: 1.0000e-05 - 242ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04793
48/48 - 0s - loss: 0.7418 - val_loss: 0.0609 - lr: 1.0000e-05 - 246ms/epoch - 5ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189
EMA
EMA([input_arrays], [timeperiod=30])

Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
51

Working on EMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.50 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.43 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15952.568, Time=15.01 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=7.85 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16628.634, Time=10.96 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16462.206, Time=24.67 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16848.298, Time=13.03 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.023, Time=6.75 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.619, Time=3.61 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=7.63 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=18.64 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.994, Time=3.95 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.667, Time=4.79 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 125.840 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        19:21:56   BIC                         -16911.966
Sample:                             0   HQIC                        -17010.204
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -2.316e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x2         -2.309e-10   6.24e-05   -3.7e-06      1.000      -0.000       0.000
x3         -2.325e-10   6.26e-05  -3.71e-06      1.000      -0.000       0.000
x4             1.0000   6.25e-05    1.6e+04      0.000       1.000       1.000
x5         -2.107e-10   5.96e-05  -3.54e-06      1.000      -0.000       0.000
x6         -7.997e-10      0.000  -7.41e-06      1.000      -0.000       0.000
x7         -2.295e-10   6.22e-05  -3.69e-06      1.000      -0.000       0.000
x8         -2.246e-10   6.15e-05  -3.65e-06      1.000      -0.000       0.000
x9         -1.167e-11   1.25e-05  -9.33e-07      1.000   -2.45e-05    2.45e-05
x10        -4.454e-11   2.66e-05  -1.68e-06      1.000   -5.21e-05    5.21e-05
x11        -2.221e-10   6.11e-05  -3.63e-06      1.000      -0.000       0.000
x12        -2.266e-10   6.18e-05  -3.66e-06      1.000      -0.000       0.000
x13        -2.315e-10   6.25e-05  -3.71e-06      1.000      -0.000       0.000
x14        -1.767e-09      0.000  -1.02e-05      1.000      -0.000       0.000
x15         -2.11e-10   5.93e-05  -3.56e-06      1.000      -0.000       0.000
x16        -5.283e-10   9.45e-05  -5.59e-06      1.000      -0.000       0.000
x17        -2.098e-10   6.01e-05  -3.49e-06      1.000      -0.000       0.000
x18         -3.82e-11   2.41e-05  -1.58e-06      1.000   -4.73e-05    4.73e-05
x19        -2.645e-10   6.61e-05     -4e-06      1.000      -0.000       0.000
x20        -2.417e-10   6.21e-05  -3.89e-06      1.000      -0.000       0.000
x21        -4.824e-10   8.83e-05  -5.46e-06      1.000      -0.000       0.000
x22        -3.758e-13   1.19e-11     -0.032      0.975   -2.36e-11    2.29e-11
x23        -1.089e-11   8.42e-11     -0.129      0.897   -1.76e-10    1.54e-10
x24        -2.538e-09      0.000  -1.44e-05      1.000      -0.000       0.000
x25        -2.038e-09      0.000  -1.49e-05      1.000      -0.000       0.000
x26         -3.16e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x27        -2.955e-09      0.000  -1.32e-05      1.000      -0.000       0.000
x28        -1.664e-09      0.000  -9.94e-06      1.000      -0.000       0.000
x29        -1.568e-09      0.000  -9.63e-06      1.000      -0.000       0.000
ar.L1         -0.4923    6.2e-10  -7.94e+08      0.000      -0.492      -0.492
ar.L2         -0.1923    3.6e-10  -5.35e+08      0.000      -0.192      -0.192
ar.L3         -0.0462   1.71e-10  -2.71e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.41e-09  -5.04e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  51.79   Jarque-Bera (JB):           4012066.18
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.44
Prob(H) (two-sided):                  0.00   Kurtosis:                       348.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 5.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05184, saving model to LSTM8.h5
16/16 - 3s - loss: 1.4618 - val_loss: 0.0518 - lr: 0.0010 - 3s/epoch - 214ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.3997 - val_loss: 0.0525 - lr: 0.0010 - 120ms/epoch - 8ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.3470 - val_loss: 0.0533 - lr: 0.0010 - 107ms/epoch - 7ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.3012 - val_loss: 0.0541 - lr: 0.0010 - 99ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.2618 - val_loss: 0.0549 - lr: 0.0010 - 99ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.2275 - val_loss: 0.0558 - lr: 0.0010 - 85ms/epoch - 5ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.2070 - val_loss: 0.0559 - lr: 1.0000e-04 - 95ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.2040 - val_loss: 0.0560 - lr: 1.0000e-04 - 94ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.2011 - val_loss: 0.0562 - lr: 1.0000e-04 - 96ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1982 - val_loss: 0.0563 - lr: 1.0000e-04 - 93ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1954 - val_loss: 0.0564 - lr: 1.0000e-04 - 95ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1936 - val_loss: 0.0564 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1933 - val_loss: 0.0564 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1930 - val_loss: 0.0564 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1927 - val_loss: 0.0564 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1924 - val_loss: 0.0564 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1921 - val_loss: 0.0564 - lr: 1.0000e-05 - 93ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1919 - val_loss: 0.0565 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1916 - val_loss: 0.0565 - lr: 1.0000e-05 - 108ms/epoch - 7ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1913 - val_loss: 0.0565 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1910 - val_loss: 0.0565 - lr: 1.0000e-05 - 109ms/epoch - 7ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1907 - val_loss: 0.0565 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1904 - val_loss: 0.0565 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1901 - val_loss: 0.0565 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1899 - val_loss: 0.0565 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1896 - val_loss: 0.0565 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1893 - val_loss: 0.0566 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1890 - val_loss: 0.0566 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1887 - val_loss: 0.0566 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1884 - val_loss: 0.0566 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1881 - val_loss: 0.0566 - lr: 1.0000e-05 - 118ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1878 - val_loss: 0.0566 - lr: 1.0000e-05 - 124ms/epoch - 8ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1876 - val_loss: 0.0566 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1873 - val_loss: 0.0566 - lr: 1.0000e-05 - 105ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1870 - val_loss: 0.0566 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1867 - val_loss: 0.0567 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1864 - val_loss: 0.0567 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1861 - val_loss: 0.0567 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1858 - val_loss: 0.0567 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1855 - val_loss: 0.0567 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1853 - val_loss: 0.0567 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1850 - val_loss: 0.0567 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1847 - val_loss: 0.0567 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1844 - val_loss: 0.0568 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1841 - val_loss: 0.0568 - lr: 1.0000e-05 - 116ms/epoch - 7ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1838 - val_loss: 0.0568 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1835 - val_loss: 0.0568 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1832 - val_loss: 0.0568 - lr: 1.0000e-05 - 91ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1830 - val_loss: 0.0568 - lr: 1.0000e-05 - 90ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1827 - val_loss: 0.0568 - lr: 1.0000e-05 - 92ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05184
16/16 - 0s - loss: 1.1824 - val_loss: 0.0568 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245
WMA
WMA([input_arrays], [timeperiod=30])

Weighted Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
49

Working on WMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.778, Time=3.35 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.587, Time=5.41 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-14597.576, Time=5.52 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.587, Time=7.97 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15338.693, Time=10.63 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15153.472, Time=26.39 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17112.658, Time=15.13 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.587, Time=10.63 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15106.216, Time=13.83 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-12251.715, Time=34.06 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 132.942 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8588.329
Date:                Sun, 12 Dec 2021   AIC                         -17112.658
Time:                        19:32:50   BIC                         -16962.551
Sample:                             0   HQIC                        -17055.011
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          -4.53e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x2         -4.512e-09   3.25e-06     -0.001      0.999   -6.38e-06    6.37e-06
x3         -4.538e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x4             1.0000   3.26e-06   3.07e+05      0.000       1.000       1.000
x5         -4.105e-09   3.11e-06     -0.001      0.999    -6.1e-06    6.09e-06
x6         -1.488e-08   5.45e-06     -0.003      0.998   -1.07e-05    1.07e-05
x7         -4.481e-09   3.24e-06     -0.001      0.999   -6.36e-06    6.36e-06
x8         -4.365e-09    3.2e-06     -0.001      0.999   -6.29e-06    6.28e-06
x9         -4.628e-10   8.38e-07     -0.001      1.000   -1.64e-06    1.64e-06
x10        -7.326e-10    1.3e-06     -0.001      1.000   -2.55e-06    2.54e-06
x11        -4.347e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x12        -4.345e-09    3.2e-06     -0.001      0.999   -6.27e-06    6.26e-06
x13         -4.52e-09   3.26e-06     -0.001      0.999   -6.39e-06    6.38e-06
x14        -3.586e-08      9e-06     -0.004      0.997   -1.77e-05    1.76e-05
x15        -3.757e-09   2.98e-06     -0.001      0.999   -5.84e-06    5.83e-06
x16         -1.24e-08   5.36e-06     -0.002      0.998   -1.05e-05    1.05e-05
x17        -4.515e-09   3.26e-06     -0.001      0.999    -6.4e-06    6.39e-06
x18        -2.632e-10   7.07e-07     -0.000      1.000   -1.39e-06    1.39e-06
x19        -4.642e-09    3.3e-06     -0.001      0.999   -6.47e-06    6.46e-06
x20        -3.919e-10   6.91e-07     -0.001      1.000   -1.36e-06    1.35e-06
x21         -7.69e-09   4.13e-06     -0.002      0.999   -8.11e-06    8.09e-06
x22        -6.998e-12   2.69e-13    -25.970      0.000   -7.53e-12   -6.47e-12
x23         -1.81e-10   2.22e-12    -81.582      0.000   -1.85e-10   -1.77e-10
x24        -4.955e-08    8.9e-06     -0.006      0.996   -1.75e-05    1.74e-05
x25        -4.901e-08    8.4e-06     -0.006      0.995   -1.65e-05    1.64e-05
x26        -6.446e-08    1.2e-05     -0.005      0.996   -2.37e-05    2.35e-05
x27         -5.73e-08   1.14e-05     -0.005      0.996   -2.24e-05    2.23e-05
x28        -2.997e-08   8.22e-06     -0.004      0.997   -1.61e-05    1.61e-05
x29        -3.486e-08   8.89e-06     -0.004      0.997   -1.75e-05    1.74e-05
ma.L1         -1.3902   3.62e-10  -3.84e+09      0.000      -1.390      -1.390
ma.L2          0.4033   3.72e-10   1.08e+09      0.000       0.403       0.403
sigma2      8.541e-11   6.95e-11      1.229      0.219   -5.08e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  66.92   Jarque-Bera (JB):           6039240.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                            12.14
Prob(H) (two-sided):                  0.00   Kurtosis:                       426.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 4.94e+30. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04179, saving model to LSTM8.h5
17/17 - 4s - loss: 1.3396 - val_loss: 0.0418 - lr: 0.0010 - 4s/epoch - 219ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.2689 - val_loss: 0.0421 - lr: 0.0010 - 101ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.2104 - val_loss: 0.0427 - lr: 0.0010 - 104ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.1615 - val_loss: 0.0435 - lr: 0.0010 - 101ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.1191 - val_loss: 0.0444 - lr: 0.0010 - 120ms/epoch - 7ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0813 - val_loss: 0.0454 - lr: 0.0010 - 115ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0585 - val_loss: 0.0455 - lr: 1.0000e-04 - 99ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0552 - val_loss: 0.0456 - lr: 1.0000e-04 - 110ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0519 - val_loss: 0.0458 - lr: 1.0000e-04 - 96ms/epoch - 6ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0488 - val_loss: 0.0459 - lr: 1.0000e-04 - 105ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0456 - val_loss: 0.0460 - lr: 1.0000e-04 - 96ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0436 - val_loss: 0.0460 - lr: 1.0000e-05 - 90ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0432 - val_loss: 0.0460 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0429 - val_loss: 0.0460 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0426 - val_loss: 0.0460 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0423 - val_loss: 0.0461 - lr: 1.0000e-05 - 97ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0420 - val_loss: 0.0461 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0417 - val_loss: 0.0461 - lr: 1.0000e-05 - 109ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0414 - val_loss: 0.0461 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0410 - val_loss: 0.0461 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0407 - val_loss: 0.0461 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0404 - val_loss: 0.0461 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0401 - val_loss: 0.0462 - lr: 1.0000e-05 - 149ms/epoch - 9ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0398 - val_loss: 0.0462 - lr: 1.0000e-05 - 119ms/epoch - 7ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0395 - val_loss: 0.0462 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0391 - val_loss: 0.0462 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0388 - val_loss: 0.0462 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0385 - val_loss: 0.0462 - lr: 1.0000e-05 - 96ms/epoch - 6ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0382 - val_loss: 0.0462 - lr: 1.0000e-05 - 90ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0379 - val_loss: 0.0463 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0376 - val_loss: 0.0463 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0372 - val_loss: 0.0463 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0369 - val_loss: 0.0463 - lr: 1.0000e-05 - 100ms/epoch - 6ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0366 - val_loss: 0.0463 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0363 - val_loss: 0.0463 - lr: 1.0000e-05 - 101ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0360 - val_loss: 0.0464 - lr: 1.0000e-05 - 107ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0357 - val_loss: 0.0464 - lr: 1.0000e-05 - 106ms/epoch - 6ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0353 - val_loss: 0.0464 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0350 - val_loss: 0.0464 - lr: 1.0000e-05 - 94ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0347 - val_loss: 0.0464 - lr: 1.0000e-05 - 95ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0344 - val_loss: 0.0464 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0341 - val_loss: 0.0465 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0337 - val_loss: 0.0465 - lr: 1.0000e-05 - 105ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0334 - val_loss: 0.0465 - lr: 1.0000e-05 - 112ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0331 - val_loss: 0.0465 - lr: 1.0000e-05 - 98ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0328 - val_loss: 0.0465 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0325 - val_loss: 0.0465 - lr: 1.0000e-05 - 103ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0322 - val_loss: 0.0466 - lr: 1.0000e-05 - 102ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0318 - val_loss: 0.0466 - lr: 1.0000e-05 - 89ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0315 - val_loss: 0.0466 - lr: 1.0000e-05 - 99ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04179
17/17 - 0s - loss: 1.0312 - val_loss: 0.0466 - lr: 1.0000e-05 - 104ms/epoch - 6ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245

WMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 58.067607397948805 
RMSE:	 7.620210456276704 
MAPE:	 6.244282675104111
DEMA
DEMA([input_arrays], [timeperiod=30])

Double Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
89

Working on DEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.776, Time=3.39 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.586, Time=5.37 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16271.755, Time=7.19 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.586, Time=8.20 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15152.908, Time=10.72 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14481.105, Time=13.34 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16088.109, Time=21.31 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-17014.021, Time=7.29 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.615, Time=3.39 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-17071.454, Time=7.26 sec
 ARIMA(3,3,2)(0,0,0)[0]             : AIC=inf, Time=17.72 sec
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/sarimax.py:1906: RuntimeWarning: divide by zero encountered in reciprocal
  return np.roots(self.polynomial_reduced_ma)**-1
 ARIMA(2,3,2)(0,0,0)[0]             : AIC=-16987.981, Time=4.26 sec
 ARIMA(3,3,1)(0,0,0)[0] intercept   : AIC=-16982.666, Time=4.59 sec

Best model:  ARIMA(3,3,1)(0,0,0)[0]          
Total fit time: 114.053 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(3, 3, 1)   Log Likelihood                8569.727
Date:                Sun, 12 Dec 2021   AIC                         -17071.454
Time:                        19:39:03   BIC                         -16911.965
Sample:                             0   HQIC                        -17010.203
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -2.8e-10   6.02e-05  -4.65e-06      1.000      -0.000       0.000
x2         -2.817e-10   6.04e-05  -4.66e-06      1.000      -0.000       0.000
x3         -2.805e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x4             1.0000   6.03e-05   1.66e+04      0.000       1.000       1.000
x5           -2.6e-10    5.8e-05  -4.48e-06      1.000      -0.000       0.000
x6         -1.389e-09      0.000  -1.08e-05      1.000      -0.000       0.000
x7         -2.789e-10   6.01e-05  -4.64e-06      1.000      -0.000       0.000
x8         -2.763e-10   5.99e-05  -4.62e-06      1.000      -0.000       0.000
x9         -2.224e-12    1.6e-06  -1.39e-06      1.000   -3.13e-06    3.13e-06
x10        -1.345e-10   4.12e-05  -3.26e-06      1.000   -8.08e-05    8.08e-05
x11          -2.9e-10   6.12e-05  -4.74e-06      1.000      -0.000       0.000
x12        -2.602e-10   5.82e-05  -4.47e-06      1.000      -0.000       0.000
x13        -2.807e-10   6.03e-05  -4.65e-06      1.000      -0.000       0.000
x14         -1.87e-09      0.000   -1.2e-05      1.000      -0.000       0.000
x15        -2.844e-10   6.05e-05   -4.7e-06      1.000      -0.000       0.000
x16        -7.962e-11    3.2e-05  -2.48e-06      1.000   -6.28e-05    6.28e-05
x17        -2.445e-10   5.61e-05  -4.36e-06      1.000      -0.000       0.000
x18          -6.4e-10   9.15e-05  -6.99e-06      1.000      -0.000       0.000
x19        -2.923e-10   6.14e-05  -4.76e-06      1.000      -0.000       0.000
x20        -4.336e-10   7.41e-05  -5.86e-06      1.000      -0.000       0.000
x21         -4.55e-10    7.5e-05  -6.07e-06      1.000      -0.000       0.000
x22        -3.587e-13   1.42e-11     -0.025      0.980   -2.82e-11    2.75e-11
x23        -1.088e-11   9.56e-11     -0.114      0.909   -1.98e-10    1.76e-10
x24        -2.146e-09      0.000  -1.63e-05      1.000      -0.000       0.000
x25        -1.637e-09      0.000  -1.35e-05      1.000      -0.000       0.000
x26        -3.147e-09      0.000  -1.56e-05      1.000      -0.000       0.000
x27         -2.58e-09      0.000  -1.41e-05      1.000      -0.000       0.000
x28        -2.444e-09      0.000  -1.37e-05      1.000      -0.000       0.000
x29        -1.666e-09      0.000  -1.13e-05      1.000      -0.000       0.000
ar.L1         -0.4923    5.1e-10  -9.65e+08      0.000      -0.492      -0.492
ar.L2         -0.1923   2.96e-10  -6.49e+08      0.000      -0.192      -0.192
ar.L3         -0.0462    1.4e-10  -3.29e+08      0.000      -0.046      -0.046
ma.L1         -0.7077   1.16e-09  -6.12e+08      0.000      -0.708      -0.708
sigma2       8.99e-11   6.96e-11      1.291      0.197   -4.66e-11    2.26e-10
===================================================================================
Ljung-Box (L1) (Q):                  54.06   Jarque-Bera (JB):           4126495.58
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.48
Prob(H) (two-sided):                  0.00   Kurtosis:                       353.58
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 2.01e+30. Standard errors may be unstable.
ARIMA order: (3, 3, 1) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04315, saving model to LSTM8.h5
10/10 - 3s - loss: 1.3932 - val_loss: 0.0432 - lr: 0.0010 - 3s/epoch - 341ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.3430 - val_loss: 0.0433 - lr: 0.0010 - 68ms/epoch - 7ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.2911 - val_loss: 0.0435 - lr: 0.0010 - 69ms/epoch - 7ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.2357 - val_loss: 0.0440 - lr: 0.0010 - 77ms/epoch - 8ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.1792 - val_loss: 0.0446 - lr: 0.0010 - 72ms/epoch - 7ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.1259 - val_loss: 0.0452 - lr: 0.0010 - 68ms/epoch - 7ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0918 - val_loss: 0.0453 - lr: 1.0000e-04 - 66ms/epoch - 7ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0874 - val_loss: 0.0454 - lr: 1.0000e-04 - 76ms/epoch - 8ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0832 - val_loss: 0.0455 - lr: 1.0000e-04 - 81ms/epoch - 8ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0791 - val_loss: 0.0455 - lr: 1.0000e-04 - 66ms/epoch - 7ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0751 - val_loss: 0.0456 - lr: 1.0000e-04 - 72ms/epoch - 7ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0723 - val_loss: 0.0456 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0719 - val_loss: 0.0456 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0715 - val_loss: 0.0456 - lr: 1.0000e-05 - 87ms/epoch - 9ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0711 - val_loss: 0.0456 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0708 - val_loss: 0.0457 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0704 - val_loss: 0.0457 - lr: 1.0000e-05 - 83ms/epoch - 8ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0700 - val_loss: 0.0457 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0696 - val_loss: 0.0457 - lr: 1.0000e-05 - 70ms/epoch - 7ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0692 - val_loss: 0.0457 - lr: 1.0000e-05 - 65ms/epoch - 7ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0688 - val_loss: 0.0457 - lr: 1.0000e-05 - 75ms/epoch - 7ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0684 - val_loss: 0.0457 - lr: 1.0000e-05 - 75ms/epoch - 8ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0680 - val_loss: 0.0457 - lr: 1.0000e-05 - 85ms/epoch - 8ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0677 - val_loss: 0.0457 - lr: 1.0000e-05 - 77ms/epoch - 8ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0673 - val_loss: 0.0457 - lr: 1.0000e-05 - 88ms/epoch - 9ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0669 - val_loss: 0.0457 - lr: 1.0000e-05 - 72ms/epoch - 7ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0665 - val_loss: 0.0457 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0661 - val_loss: 0.0458 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0657 - val_loss: 0.0458 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0653 - val_loss: 0.0458 - lr: 1.0000e-05 - 74ms/epoch - 7ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0649 - val_loss: 0.0458 - lr: 1.0000e-05 - 66ms/epoch - 7ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0646 - val_loss: 0.0458 - lr: 1.0000e-05 - 65ms/epoch - 7ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0642 - val_loss: 0.0458 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0638 - val_loss: 0.0458 - lr: 1.0000e-05 - 67ms/epoch - 7ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0634 - val_loss: 0.0458 - lr: 1.0000e-05 - 93ms/epoch - 9ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0630 - val_loss: 0.0458 - lr: 1.0000e-05 - 90ms/epoch - 9ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0626 - val_loss: 0.0458 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0623 - val_loss: 0.0458 - lr: 1.0000e-05 - 83ms/epoch - 8ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0619 - val_loss: 0.0458 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0615 - val_loss: 0.0459 - lr: 1.0000e-05 - 80ms/epoch - 8ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0611 - val_loss: 0.0459 - lr: 1.0000e-05 - 64ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0607 - val_loss: 0.0459 - lr: 1.0000e-05 - 76ms/epoch - 8ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0603 - val_loss: 0.0459 - lr: 1.0000e-05 - 89ms/epoch - 9ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0600 - val_loss: 0.0459 - lr: 1.0000e-05 - 71ms/epoch - 7ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0596 - val_loss: 0.0459 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0592 - val_loss: 0.0459 - lr: 1.0000e-05 - 68ms/epoch - 7ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0588 - val_loss: 0.0459 - lr: 1.0000e-05 - 73ms/epoch - 7ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0585 - val_loss: 0.0459 - lr: 1.0000e-05 - 69ms/epoch - 7ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0581 - val_loss: 0.0459 - lr: 1.0000e-05 - 78ms/epoch - 8ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0577 - val_loss: 0.0459 - lr: 1.0000e-05 - 81ms/epoch - 8ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04315
10/10 - 0s - loss: 1.0573 - val_loss: 0.0459 - lr: 1.0000e-05 - 94ms/epoch - 9ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245

WMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 58.067607397948805 
RMSE:	 7.620210456276704 
MAPE:	 6.244282675104111

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 166.4719121939062 
RMSE:	 12.902399474280209 
MAPE:	 11.649540302125361
KAMA
KAMA([input_arrays], [timeperiod=30])

Kaufman Adaptive Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
18

Working on KAMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.104, Time=3.73 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.591, Time=5.52 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16779.655, Time=10.82 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.590, Time=8.55 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16989.430, Time=3.57 sec
 ARIMA(2,3,0)(0,0,0)[0]             : AIC=-16990.286, Time=3.65 sec
 ARIMA(3,3,0)(0,0,0)[0]             : AIC=-16988.543, Time=4.36 sec
 ARIMA(3,3,1)(0,0,0)[0]             : AIC=-16987.154, Time=4.11 sec
 ARIMA(2,3,0)(0,0,0)[0] intercept   : AIC=-16533.935, Time=16.02 sec

Best model:  ARIMA(2,3,0)(0,0,0)[0]          
Total fit time: 60.350 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(2, 3, 0)   Log Likelihood                8527.143
Date:                Sun, 12 Dec 2021   AIC                         -16990.286
Time:                        19:48:55   BIC                         -16840.179
Sample:                             0   HQIC                        -16932.639
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -1.1e-16        nan        nan        nan         nan         nan
x2         -3.811e-16         -0        inf      0.000   -3.81e-16   -3.81e-16
x3          8.776e-16   4.38e-27      2e+11      0.000    8.78e-16    8.78e-16
x4             1.0000   4.36e-27   2.29e+26      0.000       1.000       1.000
x5          6.686e-16   4.14e-27   1.61e+11      0.000    6.69e-16    6.69e-16
x6         -5.238e-17   9.44e-27  -5.55e+09      0.000   -5.24e-17   -5.24e-17
x7         -1.709e-16   4.37e-27  -3.91e+10      0.000   -1.71e-16   -1.71e-16
x8          1.439e-15   4.33e-27   3.32e+11      0.000    1.44e-15    1.44e-15
x9         -2.924e-16   5.73e-28   -5.1e+11      0.000   -2.92e-16   -2.92e-16
x10        -1.028e-16   1.78e-27  -5.76e+10      0.000   -1.03e-16   -1.03e-16
x11        -4.338e-16   4.31e-27  -1.01e+11      0.000   -4.34e-16   -4.34e-16
x12          1.72e-16   4.33e-27   3.97e+10      0.000    1.72e-16    1.72e-16
x13        -3.011e-16   4.36e-27  -6.91e+10      0.000   -3.01e-16   -3.01e-16
x14        -2.611e-16   1.27e-26  -2.06e+10      0.000   -2.61e-16   -2.61e-16
x15          1.53e-14   4.46e-27   3.43e+12      0.000    1.53e-14    1.53e-14
x16        -1.401e-14   5.45e-27  -2.57e+12      0.000    -1.4e-14    -1.4e-14
x17         2.316e-14   4.12e-27   5.62e+12      0.000    2.32e-14    2.32e-14
x18        -3.727e-15   3.71e-27  -1.01e+12      0.000   -3.73e-15   -3.73e-15
x19        -1.361e-14   4.94e-27  -2.75e+12      0.000   -1.36e-14   -1.36e-14
x20        -5.277e-15   6.08e-27  -8.68e+11      0.000   -5.28e-15   -5.28e-15
x21         1.178e-18   3.12e-27   3.77e+08      0.000    1.18e-18    1.18e-18
x22        -8.779e-17   1.74e-29  -5.05e+12      0.000   -8.78e-17   -8.78e-17
x23         3.183e-17   5.91e-29   5.39e+11      0.000    3.18e-17    3.18e-17
x24        -1.683e-16   1.41e-26  -1.19e+10      0.000   -1.68e-16   -1.68e-16
x25         8.988e-17   1.48e-30   6.08e+13      0.000    8.99e-17    8.99e-17
x26         4.435e-17   1.58e-26    2.8e+09      0.000    4.44e-17    4.44e-17
x27         1.538e-16   8.87e-27   1.73e+10      0.000    1.54e-16    1.54e-16
x28         1.635e-16   1.22e-26   1.34e+10      0.000    1.63e-16    1.63e-16
x29         1.474e-16   6.34e-27   2.33e+10      0.000    1.47e-16    1.47e-16
ar.L1         -0.9879   1.21e-22  -8.16e+21      0.000      -0.988      -0.988
ar.L2         -0.4879   1.29e-22  -3.79e+21      0.000      -0.488      -0.488
sigma2          1e-10   6.99e-11      1.432      0.152   -3.69e-11    2.37e-10
===================================================================================
Ljung-Box (L1) (Q):                  57.29   Jarque-Bera (JB):            559955.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.13   Skew:                             0.64
Prob(H) (two-sided):                  0.00   Kurtosis:                       132.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/statespace/mlemodel.py:2968: RuntimeWarning: divide by zero encountered in true_divide
  return self.params / self.bse
ARIMA order: (2, 3, 0) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05134, saving model to LSTM8.h5
45/45 - 4s - loss: 1.4205 - val_loss: 0.0513 - lr: 0.0010 - 4s/epoch - 80ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.05134
45/45 - 0s - loss: 1.3224 - val_loss: 0.0543 - lr: 0.0010 - 239ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05134
45/45 - 0s - loss: 1.2059 - val_loss: 0.0581 - lr: 0.0010 - 231ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05134
45/45 - 0s - loss: 1.0884 - val_loss: 0.0630 - lr: 0.0010 - 254ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.9954 - val_loss: 0.0687 - lr: 0.0010 - 258ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.9266 - val_loss: 0.0749 - lr: 0.0010 - 251ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8908 - val_loss: 0.0755 - lr: 1.0000e-04 - 258ms/epoch - 6ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8858 - val_loss: 0.0761 - lr: 1.0000e-04 - 258ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8809 - val_loss: 0.0768 - lr: 1.0000e-04 - 229ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8762 - val_loss: 0.0775 - lr: 1.0000e-04 - 244ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8715 - val_loss: 0.0782 - lr: 1.0000e-04 - 257ms/epoch - 6ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8687 - val_loss: 0.0782 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8682 - val_loss: 0.0783 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8677 - val_loss: 0.0784 - lr: 1.0000e-05 - 264ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8673 - val_loss: 0.0785 - lr: 1.0000e-05 - 240ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8668 - val_loss: 0.0785 - lr: 1.0000e-05 - 271ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8664 - val_loss: 0.0786 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8659 - val_loss: 0.0787 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8654 - val_loss: 0.0788 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8650 - val_loss: 0.0789 - lr: 1.0000e-05 - 287ms/epoch - 6ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8645 - val_loss: 0.0789 - lr: 1.0000e-05 - 250ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8640 - val_loss: 0.0790 - lr: 1.0000e-05 - 269ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8636 - val_loss: 0.0791 - lr: 1.0000e-05 - 221ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8631 - val_loss: 0.0792 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8626 - val_loss: 0.0793 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8622 - val_loss: 0.0794 - lr: 1.0000e-05 - 248ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8617 - val_loss: 0.0795 - lr: 1.0000e-05 - 235ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8612 - val_loss: 0.0796 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8608 - val_loss: 0.0796 - lr: 1.0000e-05 - 262ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8603 - val_loss: 0.0797 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8598 - val_loss: 0.0798 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8594 - val_loss: 0.0799 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8589 - val_loss: 0.0800 - lr: 1.0000e-05 - 231ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8584 - val_loss: 0.0801 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8580 - val_loss: 0.0802 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8575 - val_loss: 0.0803 - lr: 1.0000e-05 - 228ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8570 - val_loss: 0.0804 - lr: 1.0000e-05 - 225ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8565 - val_loss: 0.0805 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8561 - val_loss: 0.0806 - lr: 1.0000e-05 - 245ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8556 - val_loss: 0.0807 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8551 - val_loss: 0.0808 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8547 - val_loss: 0.0809 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8542 - val_loss: 0.0810 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8537 - val_loss: 0.0811 - lr: 1.0000e-05 - 243ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8533 - val_loss: 0.0812 - lr: 1.0000e-05 - 270ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8528 - val_loss: 0.0813 - lr: 1.0000e-05 - 254ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8523 - val_loss: 0.0814 - lr: 1.0000e-05 - 278ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8519 - val_loss: 0.0815 - lr: 1.0000e-05 - 265ms/epoch - 6ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8514 - val_loss: 0.0816 - lr: 1.0000e-05 - 252ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8509 - val_loss: 0.0817 - lr: 1.0000e-05 - 253ms/epoch - 6ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05134
45/45 - 0s - loss: 0.8505 - val_loss: 0.0818 - lr: 1.0000e-05 - 261ms/epoch - 6ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245

WMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 58.067607397948805 
RMSE:	 7.620210456276704 
MAPE:	 6.244282675104111

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 166.4719121939062 
RMSE:	 12.902399474280209 
MAPE:	 11.649540302125361

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.75% Accuracy
MSE:	 17.81489427298047 
RMSE:	 4.220769393485087 
MAPE:	 3.4008273908825086
MIDPOINT
MIDPOINT([input_arrays], [timeperiod=14])

MidPoint over period (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 14
Outputs:
    real
14

Working on MIDPOINT predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16989.238, Time=3.61 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14558.578, Time=5.27 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16746.296, Time=8.10 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14556.578, Time=8.17 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16987.591, Time=3.72 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-16395.520, Time=13.13 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17063.555, Time=12.18 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14552.578, Time=10.48 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16082.554, Time=18.75 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-15249.608, Time=18.60 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 102.023 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8563.778
Date:                Sun, 12 Dec 2021   AIC                         -17063.555
Time:                        19:52:22   BIC                         -16913.448
Sample:                             0   HQIC                        -17005.908
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.495e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x2         -1.485e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x3         -1.518e-10      0.000  -1.21e-06      1.000      -0.000       0.000
x4             1.0000      0.000   8075.329      0.000       1.000       1.000
x5         -1.356e-10      0.000  -1.15e-06      1.000      -0.000       0.000
x6         -2.861e-09      0.000  -2.38e-05      1.000      -0.000       0.000
x7         -1.374e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x8         -1.371e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x9         -7.133e-11    7.1e-06  -1.01e-05      1.000   -1.39e-05    1.39e-05
x10         -1.23e-10   4.21e-05  -2.92e-06      1.000   -8.24e-05    8.24e-05
x11        -1.357e-10      0.000   -1.1e-06      1.000      -0.000       0.000
x12        -1.401e-10      0.000  -1.11e-06      1.000      -0.000       0.000
x13        -1.436e-10      0.000  -1.16e-06      1.000      -0.000       0.000
x14        -1.179e-09      0.000  -3.22e-06      1.000      -0.001       0.001
x15        -1.651e-10      0.000   -1.2e-06      1.000      -0.000       0.000
x16        -1.064e-10      0.000  -9.62e-07      1.000      -0.000       0.000
x17        -1.041e-10      0.000  -9.53e-07      1.000      -0.000       0.000
x18        -4.477e-10      0.000  -1.99e-06      1.000      -0.000       0.000
x19        -1.816e-10      0.000  -1.26e-06      1.000      -0.000       0.000
x20         -4.37e-10      0.000  -1.96e-06      1.000      -0.000       0.000
x21        -1.371e-09    9.1e-05  -1.51e-05      1.000      -0.000       0.000
x22        -1.059e-11        nan        nan        nan         nan         nan
x23        -9.902e-11   3.83e-09     -0.026      0.979   -7.61e-09    7.41e-09
x24        -5.521e-09      0.000  -1.34e-05      1.000      -0.001       0.001
x25        -4.621e-09   6.42e-05   -7.2e-05      1.000      -0.000       0.000
x26        -1.587e-09      0.000  -3.73e-06      1.000      -0.001       0.001
x27        -8.504e-10      0.000  -2.79e-06      1.000      -0.001       0.001
x28        -1.122e-09      0.000  -3.14e-06      1.000      -0.001       0.001
x29        -6.091e-10      0.000  -2.45e-06      1.000      -0.000       0.000
ma.L1         -1.3318   7.32e-07  -1.82e+06      0.000      -1.332      -1.332
ma.L2          0.3767   7.56e-07   4.98e+05      0.000       0.377       0.377
sigma2      9.093e-11   6.97e-11      1.304      0.192   -4.57e-11    2.28e-10
===================================================================================
Ljung-Box (L1) (Q):                  76.00   Jarque-Bera (JB):            304933.46
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.03   Skew:                             1.65
Prob(H) (two-sided):                  0.00   Kurtosis:                        98.29
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.19e+28. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05076, saving model to LSTM8.h5
58/58 - 4s - loss: 1.4164 - val_loss: 0.0508 - lr: 0.0010 - 4s/epoch - 69ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.3574 - val_loss: 0.0538 - lr: 0.0010 - 314ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.2772 - val_loss: 0.0576 - lr: 0.0010 - 280ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.2063 - val_loss: 0.0623 - lr: 0.0010 - 285ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.1449 - val_loss: 0.0679 - lr: 0.0010 - 320ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0907 - val_loss: 0.0743 - lr: 0.0010 - 324ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0598 - val_loss: 0.0749 - lr: 1.0000e-04 - 284ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0551 - val_loss: 0.0756 - lr: 1.0000e-04 - 305ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0505 - val_loss: 0.0763 - lr: 1.0000e-04 - 315ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0458 - val_loss: 0.0770 - lr: 1.0000e-04 - 282ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0413 - val_loss: 0.0777 - lr: 1.0000e-04 - 280ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0384 - val_loss: 0.0777 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0379 - val_loss: 0.0778 - lr: 1.0000e-05 - 311ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0375 - val_loss: 0.0779 - lr: 1.0000e-05 - 277ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0370 - val_loss: 0.0780 - lr: 1.0000e-05 - 318ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0366 - val_loss: 0.0780 - lr: 1.0000e-05 - 288ms/epoch - 5ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0361 - val_loss: 0.0781 - lr: 1.0000e-05 - 296ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0356 - val_loss: 0.0782 - lr: 1.0000e-05 - 278ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0352 - val_loss: 0.0783 - lr: 1.0000e-05 - 300ms/epoch - 5ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0347 - val_loss: 0.0783 - lr: 1.0000e-05 - 303ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0342 - val_loss: 0.0784 - lr: 1.0000e-05 - 294ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0338 - val_loss: 0.0785 - lr: 1.0000e-05 - 326ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0333 - val_loss: 0.0786 - lr: 1.0000e-05 - 311ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0329 - val_loss: 0.0787 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0324 - val_loss: 0.0787 - lr: 1.0000e-05 - 297ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0319 - val_loss: 0.0788 - lr: 1.0000e-05 - 312ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0315 - val_loss: 0.0789 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0310 - val_loss: 0.0790 - lr: 1.0000e-05 - 285ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0305 - val_loss: 0.0791 - lr: 1.0000e-05 - 353ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0301 - val_loss: 0.0791 - lr: 1.0000e-05 - 307ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0296 - val_loss: 0.0792 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0291 - val_loss: 0.0793 - lr: 1.0000e-05 - 289ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0287 - val_loss: 0.0794 - lr: 1.0000e-05 - 317ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0282 - val_loss: 0.0795 - lr: 1.0000e-05 - 304ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0277 - val_loss: 0.0796 - lr: 1.0000e-05 - 319ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0273 - val_loss: 0.0797 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0268 - val_loss: 0.0797 - lr: 1.0000e-05 - 298ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0263 - val_loss: 0.0798 - lr: 1.0000e-05 - 320ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0259 - val_loss: 0.0799 - lr: 1.0000e-05 - 344ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0254 - val_loss: 0.0800 - lr: 1.0000e-05 - 301ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0249 - val_loss: 0.0801 - lr: 1.0000e-05 - 309ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0245 - val_loss: 0.0802 - lr: 1.0000e-05 - 284ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0240 - val_loss: 0.0803 - lr: 1.0000e-05 - 280ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0235 - val_loss: 0.0803 - lr: 1.0000e-05 - 292ms/epoch - 5ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0231 - val_loss: 0.0804 - lr: 1.0000e-05 - 324ms/epoch - 6ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0226 - val_loss: 0.0805 - lr: 1.0000e-05 - 295ms/epoch - 5ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0221 - val_loss: 0.0806 - lr: 1.0000e-05 - 323ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0217 - val_loss: 0.0807 - lr: 1.0000e-05 - 284ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0212 - val_loss: 0.0808 - lr: 1.0000e-05 - 317ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0208 - val_loss: 0.0809 - lr: 1.0000e-05 - 287ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05076
58/58 - 0s - loss: 1.0203 - val_loss: 0.0810 - lr: 1.0000e-05 - 314ms/epoch - 5ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245

WMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 58.067607397948805 
RMSE:	 7.620210456276704 
MAPE:	 6.244282675104111

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 166.4719121939062 
RMSE:	 12.902399474280209 
MAPE:	 11.649540302125361

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.75% Accuracy
MSE:	 17.81489427298047 
RMSE:	 4.220769393485087 
MAPE:	 3.4008273908825086

MIDPOINT
Prediction vs Close:		50.0% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 18.523766068694844 
RMSE:	 4.303924496165662 
MAPE:	 3.4879205441290337
T3
T3([input_arrays], [timeperiod=5], [vfactor=0.7])

Triple Exponential Moving Average (T3) (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 5
    vfactor: 0.7
Outputs:
    real
19

Working on T3 predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16837.838, Time=3.58 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-14497.319, Time=3.91 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-16084.348, Time=6.66 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-15317.920, Time=11.11 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-15304.480, Time=11.43 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-15949.053, Time=12.62 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-17059.707, Time=11.70 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-15313.920, Time=14.42 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-16054.952, Time=13.12 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-11445.350, Time=32.83 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 121.412 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8561.853
Date:                Sun, 12 Dec 2021   AIC                         -17059.707
Time:                        19:58:39   BIC                         -16909.600
Sample:                             0   HQIC                        -17002.059
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1         -1.003e-07   7.69e-05     -0.001      0.999      -0.000       0.000
x2         -1.001e-07   7.44e-05     -0.001      0.999      -0.000       0.000
x3         -1.006e-07   7.84e-05     -0.001      0.999      -0.000       0.000
x4             1.0000   7.11e-05   1.41e+04      0.000       1.000       1.000
x5         -9.611e-08   6.77e-05     -0.001      0.999      -0.000       0.000
x6         -1.249e-07   4.06e-05     -0.003      0.998   -7.96e-05    7.94e-05
x7             -1e-07   7.89e-05     -0.001      0.999      -0.000       0.000
x8            -0.0002   9.43e-05     -1.838      0.066      -0.000    1.15e-05
x9          2.853e-08   9.89e-05      0.000      1.000      -0.000       0.000
x10        -4.022e-05      0.000     -0.200      0.842      -0.000       0.000
x11            0.0003      7e-05      4.122      0.000       0.000       0.000
x12          7.55e-05      0.000      0.633      0.527      -0.000       0.000
x13        -1.005e-07   7.29e-05     -0.001      0.999      -0.000       0.000
x14        -2.756e-07      0.000     -0.001      0.999      -0.000       0.000
x15        -8.419e-08   8.98e-05     -0.001      0.999      -0.000       0.000
x16        -2.171e-07      0.000     -0.001      0.999      -0.000       0.000
x17        -1.105e-07   9.93e-05     -0.001      0.999      -0.000       0.000
x18         1.263e-07   3.22e-05      0.004      0.997   -6.31e-05    6.33e-05
x19        -8.769e-08      0.000     -0.001      0.999      -0.000       0.000
x20        -5.772e-08      0.000     -0.000      1.000      -0.000       0.000
x21         -9.77e-08      0.000     -0.001      1.000      -0.000       0.000
x22        -3.686e-12   7.09e-07   -5.2e-06      1.000   -1.39e-06    1.39e-06
x23        -9.216e-12    2.4e-05  -3.83e-07      1.000   -4.71e-05    4.71e-05
x24        -3.648e-07      0.000     -0.001      0.999      -0.001       0.001
x25        -1.391e-07      0.001     -0.000      1.000      -0.002       0.002
x26        -3.142e-07      0.000     -0.001      0.999      -0.001       0.001
x27        -3.042e-07   5.47e-05     -0.006      0.996      -0.000       0.000
x28        -1.785e-07      0.000     -0.001      0.999      -0.000       0.000
x29        -1.909e-07      0.000     -0.001      1.000      -0.001       0.001
ma.L1         -1.3901   8.24e-06  -1.69e+05      0.000      -1.390      -1.390
ma.L2          0.4035   2.01e-05   2.01e+04      0.000       0.403       0.404
sigma2      7.538e-11   6.94e-11      1.085      0.278   -6.07e-11    2.11e-10
===================================================================================
Ljung-Box (L1) (Q):                  69.36   Jarque-Bera (JB):           6470073.86
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                           -12.55
Prob(H) (two-sided):                  0.00   Kurtosis:                       441.48
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 3.58e+22. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.05379, saving model to LSTM8.h5
43/43 - 3s - loss: 1.3977 - val_loss: 0.0538 - lr: 0.0010 - 3s/epoch - 80ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.3404 - val_loss: 0.0575 - lr: 0.0010 - 248ms/epoch - 6ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.2883 - val_loss: 0.0623 - lr: 0.0010 - 252ms/epoch - 6ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.2305 - val_loss: 0.0687 - lr: 0.0010 - 240ms/epoch - 6ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.1712 - val_loss: 0.0768 - lr: 0.0010 - 247ms/epoch - 6ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.1137 - val_loss: 0.0860 - lr: 0.0010 - 243ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0791 - val_loss: 0.0870 - lr: 1.0000e-04 - 224ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0739 - val_loss: 0.0879 - lr: 1.0000e-04 - 254ms/epoch - 6ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0689 - val_loss: 0.0889 - lr: 1.0000e-04 - 217ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0639 - val_loss: 0.0898 - lr: 1.0000e-04 - 255ms/epoch - 6ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0589 - val_loss: 0.0908 - lr: 1.0000e-04 - 224ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0558 - val_loss: 0.0909 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0553 - val_loss: 0.0910 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0548 - val_loss: 0.0911 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0544 - val_loss: 0.0912 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0539 - val_loss: 0.0913 - lr: 1.0000e-05 - 256ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0534 - val_loss: 0.0914 - lr: 1.0000e-05 - 222ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0529 - val_loss: 0.0915 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0524 - val_loss: 0.0916 - lr: 1.0000e-05 - 244ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0519 - val_loss: 0.0917 - lr: 1.0000e-05 - 219ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0514 - val_loss: 0.0918 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0509 - val_loss: 0.0919 - lr: 1.0000e-05 - 241ms/epoch - 6ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0504 - val_loss: 0.0920 - lr: 1.0000e-05 - 234ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0500 - val_loss: 0.0921 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0495 - val_loss: 0.0922 - lr: 1.0000e-05 - 246ms/epoch - 6ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0490 - val_loss: 0.0923 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0485 - val_loss: 0.0923 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0480 - val_loss: 0.0924 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0475 - val_loss: 0.0925 - lr: 1.0000e-05 - 232ms/epoch - 5ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0470 - val_loss: 0.0926 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0465 - val_loss: 0.0927 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0460 - val_loss: 0.0928 - lr: 1.0000e-05 - 213ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0455 - val_loss: 0.0929 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0450 - val_loss: 0.0930 - lr: 1.0000e-05 - 239ms/epoch - 6ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0445 - val_loss: 0.0931 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0440 - val_loss: 0.0932 - lr: 1.0000e-05 - 288ms/epoch - 7ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0435 - val_loss: 0.0933 - lr: 1.0000e-05 - 218ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0430 - val_loss: 0.0934 - lr: 1.0000e-05 - 249ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0426 - val_loss: 0.0935 - lr: 1.0000e-05 - 251ms/epoch - 6ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0421 - val_loss: 0.0936 - lr: 1.0000e-05 - 216ms/epoch - 5ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0416 - val_loss: 0.0937 - lr: 1.0000e-05 - 224ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0411 - val_loss: 0.0938 - lr: 1.0000e-05 - 239ms/epoch - 6ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0406 - val_loss: 0.0939 - lr: 1.0000e-05 - 239ms/epoch - 6ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0401 - val_loss: 0.0940 - lr: 1.0000e-05 - 238ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0396 - val_loss: 0.0941 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0391 - val_loss: 0.0942 - lr: 1.0000e-05 - 242ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0386 - val_loss: 0.0943 - lr: 1.0000e-05 - 247ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0381 - val_loss: 0.0944 - lr: 1.0000e-05 - 227ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0376 - val_loss: 0.0945 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0371 - val_loss: 0.0946 - lr: 1.0000e-05 - 217ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.05379
43/43 - 0s - loss: 1.0366 - val_loss: 0.0947 - lr: 1.0000e-05 - 220ms/epoch - 5ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245

WMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 58.067607397948805 
RMSE:	 7.620210456276704 
MAPE:	 6.244282675104111

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 166.4719121939062 
RMSE:	 12.902399474280209 
MAPE:	 11.649540302125361

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.75% Accuracy
MSE:	 17.81489427298047 
RMSE:	 4.220769393485087 
MAPE:	 3.4008273908825086

MIDPOINT
Prediction vs Close:		50.0% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 18.523766068694844 
RMSE:	 4.303924496165662 
MAPE:	 3.4879205441290337

T3
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 51.75272426881254 
RMSE:	 7.193936632248893 
MAPE:	 5.759673530885367
TEMA
TEMA([input_arrays], [timeperiod=30])

Triple Exponential Moving Average (Overlap Studies)

Inputs:
    price: (any ndarray)
Parameters:
    timeperiod: 30
Outputs:
    real
9

Working on TEMA predictions
parameters used :  808 269
Performing stepwise search to minimize aic
 ARIMA(1,3,1)(0,0,0)[0]             : AIC=-16736.686, Time=3.37 sec
 ARIMA(0,3,0)(0,0,0)[0]             : AIC=-15327.143, Time=3.35 sec
 ARIMA(1,3,0)(0,0,0)[0]             : AIC=-15166.078, Time=7.26 sec
 ARIMA(0,3,1)(0,0,0)[0]             : AIC=-14962.662, Time=14.08 sec
 ARIMA(2,3,1)(0,0,0)[0]             : AIC=-16731.606, Time=5.24 sec
 ARIMA(1,3,2)(0,0,0)[0]             : AIC=-14848.952, Time=9.82 sec
 ARIMA(0,3,2)(0,0,0)[0]             : AIC=-16921.745, Time=6.38 sec
 ARIMA(0,3,3)(0,0,0)[0]             : AIC=-14958.662, Time=17.57 sec
 ARIMA(1,3,3)(0,0,0)[0]             : AIC=-15003.046, Time=13.49 sec
 ARIMA(0,3,2)(0,0,0)[0] intercept   : AIC=-16752.122, Time=3.84 sec

Best model:  ARIMA(0,3,2)(0,0,0)[0]          
Total fit time: 84.411 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  808
Model:               SARIMAX(0, 3, 2)   Log Likelihood                8492.873
Date:                Sun, 12 Dec 2021   AIC                         -16921.745
Time:                        20:04:23   BIC                         -16771.638
Sample:                             0   HQIC                        -16864.098
                                - 808                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
x1          2.277e-08      0.001   3.25e-05      1.000      -0.001       0.001
x2          2.286e-08      0.001    2.5e-05      1.000      -0.002       0.002
x3          2.286e-08      0.001   3.44e-05      1.000      -0.001       0.001
x4             1.0000      0.000   3190.279      0.000       0.999       1.001
x5          2.174e-08      0.001   4.21e-05      1.000      -0.001       0.001
x6          6.124e-09   3.05e-05      0.000      1.000   -5.97e-05    5.97e-05
x7          2.246e-08      0.001   1.67e-05      1.000      -0.003       0.003
x8            -0.0013      0.001     -1.669      0.095      -0.003       0.000
x9         -5.239e-09      0.000  -1.79e-05      1.000      -0.001       0.001
x10            0.0001    9.9e-05      1.396      0.163   -5.59e-05       0.000
x11           -0.0001      0.001     -0.177      0.859      -0.002       0.001
x12            0.0012      0.001      1.426      0.154      -0.000       0.003
x13         2.284e-08      0.000   6.75e-05      1.000      -0.001       0.001
x14         6.258e-08      0.001   5.07e-05      1.000      -0.002       0.002
x15         2.215e-08      0.000      0.000      1.000      -0.000       0.000
x16         3.243e-08      0.000      0.000      1.000      -0.001       0.001
x17          2.22e-08      0.000      0.000      1.000      -0.000       0.000
x18         7.527e-09      0.000   1.67e-05      1.000      -0.001       0.001
x19         2.477e-08      0.000      0.000      1.000      -0.000       0.000
x20        -2.348e-08      0.000  -5.78e-05      1.000      -0.001       0.001
x21         2.718e-08    5.8e-05      0.000      1.000      -0.000       0.000
x22        -2.176e-10      0.000  -5.27e-07      1.000      -0.001       0.001
x23         -2.69e-09   8.49e-05  -3.17e-05      1.000      -0.000       0.000
x24        -4.516e-08   7.24e-06     -0.006      0.995   -1.42e-05    1.41e-05
x25        -4.213e-08   2.81e-05     -0.002      0.999   -5.51e-05     5.5e-05
x26         7.946e-08      0.001      0.000      1.000      -0.001       0.001
x27         4.528e-08      0.001   6.21e-05      1.000      -0.001       0.001
x28          5.92e-08      0.001   4.12e-05      1.000      -0.003       0.003
x29         3.468e-08      0.000   7.06e-05      1.000      -0.001       0.001
ma.L1         -1.3739   4.46e-06  -3.08e+05      0.000      -1.374      -1.374
ma.L2          0.3968    1.4e-05   2.84e+04      0.000       0.397       0.397
sigma2      7.701e-11   7.39e-11      1.043      0.297   -6.78e-11    2.22e-10
===================================================================================
Ljung-Box (L1) (Q):                  61.47   Jarque-Bera (JB):           5565463.09
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.01   Skew:                            10.97
Prob(H) (two-sided):                  0.00   Kurtosis:                       409.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 6.67e+20. Standard errors may be unstable.
ARIMA order: (0, 3, 2) 

Epoch 1/500

Epoch 00001: val_loss improved from inf to 0.04760, saving model to LSTM8.h5
90/90 - 4s - loss: 1.3235 - val_loss: 0.0476 - lr: 0.0010 - 4s/epoch - 42ms/step
Epoch 2/500

Epoch 00002: val_loss did not improve from 0.04760
90/90 - 0s - loss: 1.1133 - val_loss: 0.0536 - lr: 0.0010 - 422ms/epoch - 5ms/step
Epoch 3/500

Epoch 00003: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.9187 - val_loss: 0.0610 - lr: 0.0010 - 420ms/epoch - 5ms/step
Epoch 4/500

Epoch 00004: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.8016 - val_loss: 0.0697 - lr: 0.0010 - 463ms/epoch - 5ms/step
Epoch 5/500

Epoch 00005: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.7373 - val_loss: 0.0774 - lr: 0.0010 - 435ms/epoch - 5ms/step
Epoch 6/500

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.

Epoch 00006: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6979 - val_loss: 0.0828 - lr: 0.0010 - 539ms/epoch - 6ms/step
Epoch 7/500

Epoch 00007: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6798 - val_loss: 0.0832 - lr: 1.0000e-04 - 429ms/epoch - 5ms/step
Epoch 8/500

Epoch 00008: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6773 - val_loss: 0.0835 - lr: 1.0000e-04 - 450ms/epoch - 5ms/step
Epoch 9/500

Epoch 00009: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6747 - val_loss: 0.0839 - lr: 1.0000e-04 - 453ms/epoch - 5ms/step
Epoch 10/500

Epoch 00010: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6720 - val_loss: 0.0842 - lr: 1.0000e-04 - 472ms/epoch - 5ms/step
Epoch 11/500

Epoch 00011: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.

Epoch 00011: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6694 - val_loss: 0.0845 - lr: 1.0000e-04 - 455ms/epoch - 5ms/step
Epoch 12/500

Epoch 00012: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6678 - val_loss: 0.0845 - lr: 1.0000e-05 - 522ms/epoch - 6ms/step
Epoch 13/500

Epoch 00013: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6675 - val_loss: 0.0845 - lr: 1.0000e-05 - 478ms/epoch - 5ms/step
Epoch 14/500

Epoch 00014: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6673 - val_loss: 0.0846 - lr: 1.0000e-05 - 512ms/epoch - 6ms/step
Epoch 15/500

Epoch 00015: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6670 - val_loss: 0.0846 - lr: 1.0000e-05 - 443ms/epoch - 5ms/step
Epoch 16/500

Epoch 00016: ReduceLROnPlateau reducing learning rate to 1e-05.

Epoch 00016: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6667 - val_loss: 0.0846 - lr: 1.0000e-05 - 511ms/epoch - 6ms/step
Epoch 17/500

Epoch 00017: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6664 - val_loss: 0.0847 - lr: 1.0000e-05 - 425ms/epoch - 5ms/step
Epoch 18/500

Epoch 00018: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6661 - val_loss: 0.0847 - lr: 1.0000e-05 - 515ms/epoch - 6ms/step
Epoch 19/500

Epoch 00019: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6658 - val_loss: 0.0847 - lr: 1.0000e-05 - 541ms/epoch - 6ms/step
Epoch 20/500

Epoch 00020: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6655 - val_loss: 0.0847 - lr: 1.0000e-05 - 443ms/epoch - 5ms/step
Epoch 21/500

Epoch 00021: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6652 - val_loss: 0.0848 - lr: 1.0000e-05 - 437ms/epoch - 5ms/step
Epoch 22/500

Epoch 00022: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6649 - val_loss: 0.0848 - lr: 1.0000e-05 - 441ms/epoch - 5ms/step
Epoch 23/500

Epoch 00023: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6645 - val_loss: 0.0848 - lr: 1.0000e-05 - 440ms/epoch - 5ms/step
Epoch 24/500

Epoch 00024: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6642 - val_loss: 0.0849 - lr: 1.0000e-05 - 437ms/epoch - 5ms/step
Epoch 25/500

Epoch 00025: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6639 - val_loss: 0.0849 - lr: 1.0000e-05 - 444ms/epoch - 5ms/step
Epoch 26/500

Epoch 00026: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6636 - val_loss: 0.0849 - lr: 1.0000e-05 - 429ms/epoch - 5ms/step
Epoch 27/500

Epoch 00027: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6632 - val_loss: 0.0849 - lr: 1.0000e-05 - 486ms/epoch - 5ms/step
Epoch 28/500

Epoch 00028: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6629 - val_loss: 0.0850 - lr: 1.0000e-05 - 423ms/epoch - 5ms/step
Epoch 29/500

Epoch 00029: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6625 - val_loss: 0.0850 - lr: 1.0000e-05 - 508ms/epoch - 6ms/step
Epoch 30/500

Epoch 00030: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6622 - val_loss: 0.0850 - lr: 1.0000e-05 - 429ms/epoch - 5ms/step
Epoch 31/500

Epoch 00031: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6619 - val_loss: 0.0850 - lr: 1.0000e-05 - 517ms/epoch - 6ms/step
Epoch 32/500

Epoch 00032: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6615 - val_loss: 0.0851 - lr: 1.0000e-05 - 433ms/epoch - 5ms/step
Epoch 33/500

Epoch 00033: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6612 - val_loss: 0.0851 - lr: 1.0000e-05 - 409ms/epoch - 5ms/step
Epoch 34/500

Epoch 00034: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6608 - val_loss: 0.0851 - lr: 1.0000e-05 - 435ms/epoch - 5ms/step
Epoch 35/500

Epoch 00035: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6605 - val_loss: 0.0851 - lr: 1.0000e-05 - 430ms/epoch - 5ms/step
Epoch 36/500

Epoch 00036: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6601 - val_loss: 0.0852 - lr: 1.0000e-05 - 532ms/epoch - 6ms/step
Epoch 37/500

Epoch 00037: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6598 - val_loss: 0.0852 - lr: 1.0000e-05 - 441ms/epoch - 5ms/step
Epoch 38/500

Epoch 00038: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6594 - val_loss: 0.0852 - lr: 1.0000e-05 - 504ms/epoch - 6ms/step
Epoch 39/500

Epoch 00039: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6591 - val_loss: 0.0852 - lr: 1.0000e-05 - 431ms/epoch - 5ms/step
Epoch 40/500

Epoch 00040: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6588 - val_loss: 0.0853 - lr: 1.0000e-05 - 519ms/epoch - 6ms/step
Epoch 41/500

Epoch 00041: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6584 - val_loss: 0.0853 - lr: 1.0000e-05 - 416ms/epoch - 5ms/step
Epoch 42/500

Epoch 00042: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6581 - val_loss: 0.0853 - lr: 1.0000e-05 - 443ms/epoch - 5ms/step
Epoch 43/500

Epoch 00043: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6577 - val_loss: 0.0853 - lr: 1.0000e-05 - 430ms/epoch - 5ms/step
Epoch 44/500

Epoch 00044: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6574 - val_loss: 0.0853 - lr: 1.0000e-05 - 505ms/epoch - 6ms/step
Epoch 45/500

Epoch 00045: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6570 - val_loss: 0.0854 - lr: 1.0000e-05 - 446ms/epoch - 5ms/step
Epoch 46/500

Epoch 00046: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6567 - val_loss: 0.0854 - lr: 1.0000e-05 - 503ms/epoch - 6ms/step
Epoch 47/500

Epoch 00047: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6563 - val_loss: 0.0854 - lr: 1.0000e-05 - 522ms/epoch - 6ms/step
Epoch 48/500

Epoch 00048: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6560 - val_loss: 0.0854 - lr: 1.0000e-05 - 485ms/epoch - 5ms/step
Epoch 49/500

Epoch 00049: val_loss did not improve from 0.04760
90/90 - 1s - loss: 0.6556 - val_loss: 0.0855 - lr: 1.0000e-05 - 533ms/epoch - 6ms/step
Epoch 50/500

Epoch 00050: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6553 - val_loss: 0.0855 - lr: 1.0000e-05 - 440ms/epoch - 5ms/step
Epoch 51/500

Epoch 00051: val_loss did not improve from 0.04760
90/90 - 0s - loss: 0.6549 - val_loss: 0.0855 - lr: 1.0000e-05 - 435ms/epoch - 5ms/step
Epoch 00051: early stopping
SMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	49.63% Accuracy
MSE:	 30.79397335917816 
RMSE:	 5.549231780992587 
MAPE:	 4.345848876898189

EMA
Prediction vs Close:		55.22% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 32.37277762407691 
RMSE:	 5.689708043834667 
MAPE:	 4.4297291061987245

WMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	47.01% Accuracy
MSE:	 58.067607397948805 
RMSE:	 7.620210456276704 
MAPE:	 6.244282675104111

DEMA
Prediction vs Close:		53.36% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 166.4719121939062 
RMSE:	 12.902399474280209 
MAPE:	 11.649540302125361

KAMA
Prediction vs Close:		54.1% Accuracy
Prediction vs Prediction:	50.75% Accuracy
MSE:	 17.81489427298047 
RMSE:	 4.220769393485087 
MAPE:	 3.4008273908825086

MIDPOINT
Prediction vs Close:		50.0% Accuracy
Prediction vs Prediction:	48.13% Accuracy
MSE:	 18.523766068694844 
RMSE:	 4.303924496165662 
MAPE:	 3.4879205441290337

T3
Prediction vs Close:		52.24% Accuracy
Prediction vs Prediction:	44.4% Accuracy
MSE:	 51.75272426881254 
RMSE:	 7.193936632248893 
MAPE:	 5.759673530885367

TEMA
Prediction vs Close:		51.49% Accuracy
Prediction vs Prediction:	49.25% Accuracy
MSE:	 28.424875173467463 
RMSE:	 5.331498398524327 
MAPE:	 4.66633698560039
Runtime: mins: 55.4519855402333

Architecture Used

In [136]:
from google.colab import files
import cv2
uploaded = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving Experiment8.png to Experiment8 (1).png
In [137]:
img = cv2.imread('Experiment8.png')
plt.figure(figsize=(20,10))
plt.axis("off")
plt.title('LSTM Architecture '+imgfile,fontsize=18)
plt.imshow(img)
Out[137]:
<matplotlib.image.AxesImage at 0x7fa5ce50e910>

Model Plots

In [138]:
for i in range(len(list(simulation8.keys()))):
  SIM = list(simulation8.keys())[i]
  plot_train(simulation8,SIM)
  plot_test(simulation8,SIM)
----- Train RMSE for SMA ----- 16.984837129738686
----- Train_MSE_LSTM for SMA ----- 288.48469232374987
----- Train MAE LSTM for SMA ----- 16.91967470811145
----- Test RMSE for SMA----- 5.549231780992587
----- Test_MSE_LSTM for SMA----- 30.79397335917816
----- Test_MAE_LSTM for SMA----- 4.345848876898189
----- Train RMSE for EMA ----- 24.064453268878548
----- Train_MSE_LSTM for EMA ----- 579.0979111300394
----- Train MAE LSTM for EMA ----- 24.05741292651337
----- Test RMSE for EMA----- 5.689708043834667
----- Test_MSE_LSTM for EMA----- 32.37277762407691
----- Test_MAE_LSTM for EMA----- 4.4297291061987245
----- Train RMSE for WMA ----- 21.99024849687679
----- Train_MSE_LSTM for WMA ----- 483.571028954392
----- Train MAE LSTM for WMA ----- 21.95148728153493
----- Test RMSE for WMA----- 7.620210456276704
----- Test_MSE_LSTM for WMA----- 58.067607397948805
----- Test_MAE_LSTM for WMA----- 6.244282675104111
----- Train RMSE for DEMA ----- 24.365605462542323
----- Train_MSE_LSTM for DEMA ----- 593.6827295562723
----- Train MAE LSTM for DEMA ----- 24.332061871443646
----- Test RMSE for DEMA----- 12.902399474280209
----- Test_MSE_LSTM for DEMA----- 166.4719121939062
----- Test_MAE_LSTM for DEMA----- 11.649540302125361
----- Train RMSE for KAMA ----- 17.353875298772294
----- Train_MSE_LSTM for KAMA ----- 301.1569878853392
----- Train MAE LSTM for KAMA ----- 17.33646524542629
----- Test RMSE for KAMA----- 4.220769393485087
----- Test_MSE_LSTM for KAMA----- 17.81489427298047
----- Test_MAE_LSTM for KAMA----- 3.4008273908825086
----- Train RMSE for MIDPOINT ----- 19.91769524640393
----- Train_MSE_LSTM for MIDPOINT ----- 396.7145839286217
----- Train MAE LSTM for MIDPOINT ----- 19.915788006074358
----- Test RMSE for MIDPOINT----- 4.303924496165662
----- Test_MSE_LSTM for MIDPOINT----- 18.523766068694844
----- Test_MAE_LSTM for MIDPOINT----- 3.4879205441290337
----- Train RMSE for T3 ----- 22.915612404801223
----- Train_MSE_LSTM for T3 ----- 525.1252918870797
----- Train MAE LSTM for T3 ----- 22.913270100508587
----- Test RMSE for T3----- 7.193936632248893
----- Test_MSE_LSTM for T3----- 51.75272426881254
----- Test_MAE_LSTM for T3----- 5.759673530885367
----- Train RMSE for TEMA ----- 18.607130117898784
----- Train_MSE_LSTM for TEMA ----- 346.22529122441597
----- Train MAE LSTM for TEMA ----- 18.587996395507663
----- Test RMSE for TEMA----- 5.331498398524327
----- Test_MSE_LSTM for TEMA----- 28.424875173467463
----- Test_MAE_LSTM for TEMA----- 4.66633698560039

List of RMSE, MSE & MAE scores for Test data

In [138]:
import json
with open('simulation1_data.json') as json_file:
    simulation1 = json.load(json_file)

with open('simulation2_data.json') as json_file:
    simulation2 = json.load(json_file)

with open('simulation3_data.json') as json_file:
    simulation3 = json.load(json_file)

with open('simulation4_data.json') as json_file:
    simulation4 = json.load(json_file)

with open('simulation5_data.json') as json_file:
    simulation5 = json.load(json_file)

with open('simulation6_data.json') as json_file:
    simulation6 = json.load(json_file)

with open('simulation7_data.json') as json_file:
    simulation7 = json.load(json_file)

with open('simulation8_data.json') as json_file:
    simulation8 = json.load(json_file)
In [140]:
text = 'Stock with Full dataset'
simulations = [simulation1,simulation2,simulation3,simulation4,simulation5,simulation6,simulation7,simulation8]
for i,simulation in enumerate(simulations):
  for ma in simulation.keys():
    print(text+'Experiment ',i+1,' for MA :',ma,'the MSE  is: ',simulation[ma]['final']['mse'])
    print(text+'Experiment ',i+1,' for MA :',ma,'the RMSE is: ',simulation[ma]['final']['rmse'])
    print(text+'Experiment ',i+1,' for MA :',ma,'the MAE is: ',simulation[ma]['final']['mae'])
Stock with Full datasetExperiment  1  for MA : SMA the MSE  is:  29.509403666146007
Stock with Full datasetExperiment  1  for MA : SMA the RMSE is:  5.432255854260365
Stock with Full datasetExperiment  1  for MA : SMA the MAE is:  4.5288133477558885
Stock with Full datasetExperiment  1  for MA : EMA the MSE  is:  28.603272766421263
Stock with Full datasetExperiment  1  for MA : EMA the RMSE is:  5.348202760406646
Stock with Full datasetExperiment  1  for MA : EMA the MAE is:  4.3952252144553965
Stock with Full datasetExperiment  1  for MA : WMA the MSE  is:  80.72598349686672
Stock with Full datasetExperiment  1  for MA : WMA the RMSE is:  8.9847639644493
Stock with Full datasetExperiment  1  for MA : WMA the MAE is:  7.266216353433966
Stock with Full datasetExperiment  1  for MA : DEMA the MSE  is:  74.8946292448382
Stock with Full datasetExperiment  1  for MA : DEMA the RMSE is:  8.654168316183721
Stock with Full datasetExperiment  1  for MA : DEMA the MAE is:  7.175854729849037
Stock with Full datasetExperiment  1  for MA : KAMA the MSE  is:  23.77011386693893
Stock with Full datasetExperiment  1  for MA : KAMA the RMSE is:  4.87546037487117
Stock with Full datasetExperiment  1  for MA : KAMA the MAE is:  3.900500517739451
Stock with Full datasetExperiment  1  for MA : MIDPOINT the MSE  is:  53.557107319764015
Stock with Full datasetExperiment  1  for MA : MIDPOINT the RMSE is:  7.318272153983071
Stock with Full datasetExperiment  1  for MA : MIDPOINT the MAE is:  6.3365268769325365
Stock with Full datasetExperiment  1  for MA : T3 the MSE  is:  44.09609147416104
Stock with Full datasetExperiment  1  for MA : T3 the RMSE is:  6.640488797834165
Stock with Full datasetExperiment  1  for MA : T3 the MAE is:  5.406095596816415
Stock with Full datasetExperiment  1  for MA : TEMA the MSE  is:  9.564405293897392
Stock with Full datasetExperiment  1  for MA : TEMA the RMSE is:  3.0926372716336124
Stock with Full datasetExperiment  1  for MA : TEMA the MAE is:  2.44888799215368
Stock with Full datasetExperiment  2  for MA : SMA the MSE  is:  75.20110458138421
Stock with Full datasetExperiment  2  for MA : SMA the RMSE is:  8.67185704341257
Stock with Full datasetExperiment  2  for MA : SMA the MAE is:  7.0799160587584336
Stock with Full datasetExperiment  2  for MA : EMA the MSE  is:  61.82384712230415
Stock with Full datasetExperiment  2  for MA : EMA the RMSE is:  7.862814198638052
Stock with Full datasetExperiment  2  for MA : EMA the MAE is:  6.504666247736678
Stock with Full datasetExperiment  2  for MA : WMA the MSE  is:  78.06346997131263
Stock with Full datasetExperiment  2  for MA : WMA the RMSE is:  8.835353415190172
Stock with Full datasetExperiment  2  for MA : WMA the MAE is:  6.948265794170055
Stock with Full datasetExperiment  2  for MA : DEMA the MSE  is:  153.59400995187858
Stock with Full datasetExperiment  2  for MA : DEMA the RMSE is:  12.39330504554288
Stock with Full datasetExperiment  2  for MA : DEMA the MAE is:  11.203775482220726
Stock with Full datasetExperiment  2  for MA : KAMA the MSE  is:  121.28941541171922
Stock with Full datasetExperiment  2  for MA : KAMA the RMSE is:  11.013147388994629
Stock with Full datasetExperiment  2  for MA : KAMA the MAE is:  9.175643045864026
Stock with Full datasetExperiment  2  for MA : MIDPOINT the MSE  is:  110.09412594018622
Stock with Full datasetExperiment  2  for MA : MIDPOINT the RMSE is:  10.492574800314088
Stock with Full datasetExperiment  2  for MA : MIDPOINT the MAE is:  8.796456301428389
Stock with Full datasetExperiment  2  for MA : T3 the MSE  is:  225.73907153615718
Stock with Full datasetExperiment  2  for MA : T3 the RMSE is:  15.024615520410403
Stock with Full datasetExperiment  2  for MA : T3 the MAE is:  12.611725131734374
Stock with Full datasetExperiment  2  for MA : TEMA the MSE  is:  157.04980120863047
Stock with Full datasetExperiment  2  for MA : TEMA the RMSE is:  12.531951213144364
Stock with Full datasetExperiment  2  for MA : TEMA the MAE is:  11.294114614846999
Stock with Full datasetExperiment  3  for MA : SMA the MSE  is:  32.725767438505336
Stock with Full datasetExperiment  3  for MA : SMA the RMSE is:  5.7206439706125165
Stock with Full datasetExperiment  3  for MA : SMA the MAE is:  4.798603095387009
Stock with Full datasetExperiment  3  for MA : EMA the MSE  is:  143.9522591181831
Stock with Full datasetExperiment  3  for MA : EMA the RMSE is:  11.998010631691534
Stock with Full datasetExperiment  3  for MA : EMA the MAE is:  10.07848404711658
Stock with Full datasetExperiment  3  for MA : WMA the MSE  is:  24.586224407987817
Stock with Full datasetExperiment  3  for MA : WMA the RMSE is:  4.958449798877449
Stock with Full datasetExperiment  3  for MA : WMA the MAE is:  3.970226889097132
Stock with Full datasetExperiment  3  for MA : DEMA the MSE  is:  207.2547601932076
Stock with Full datasetExperiment  3  for MA : DEMA the RMSE is:  14.3963453762824
Stock with Full datasetExperiment  3  for MA : DEMA the MAE is:  12.894635987621164
Stock with Full datasetExperiment  3  for MA : KAMA the MSE  is:  23.743754657069395
Stock with Full datasetExperiment  3  for MA : KAMA the RMSE is:  4.872756371610364
Stock with Full datasetExperiment  3  for MA : KAMA the MAE is:  3.7850733762502107
Stock with Full datasetExperiment  3  for MA : MIDPOINT the MSE  is:  35.62442093531873
Stock with Full datasetExperiment  3  for MA : MIDPOINT the RMSE is:  5.968619684258559
Stock with Full datasetExperiment  3  for MA : MIDPOINT the MAE is:  5.0490603478808165
Stock with Full datasetExperiment  3  for MA : T3 the MSE  is:  103.73535640918065
Stock with Full datasetExperiment  3  for MA : T3 the RMSE is:  10.185055542763655
Stock with Full datasetExperiment  3  for MA : T3 the MAE is:  8.016244139827235
Stock with Full datasetExperiment  3  for MA : TEMA the MSE  is:  39.894741171517865
Stock with Full datasetExperiment  3  for MA : TEMA the RMSE is:  6.316228397668807
Stock with Full datasetExperiment  3  for MA : TEMA the MAE is:  5.481705479796751
Stock with Full datasetExperiment  4  for MA : SMA the MSE  is:  19.776724587061057
Stock with Full datasetExperiment  4  for MA : SMA the RMSE is:  4.447102943159856
Stock with Full datasetExperiment  4  for MA : SMA the MAE is:  3.587879520041786
Stock with Full datasetExperiment  4  for MA : EMA the MSE  is:  31.621751516368622
Stock with Full datasetExperiment  4  for MA : EMA the RMSE is:  5.623322106759368
Stock with Full datasetExperiment  4  for MA : EMA the MAE is:  4.355106062590965
Stock with Full datasetExperiment  4  for MA : WMA the MSE  is:  52.4753296205182
Stock with Full datasetExperiment  4  for MA : WMA the RMSE is:  7.2439857551294375
Stock with Full datasetExperiment  4  for MA : WMA the MAE is:  5.852253139584933
Stock with Full datasetExperiment  4  for MA : DEMA the MSE  is:  146.44755629127866
Stock with Full datasetExperiment  4  for MA : DEMA the RMSE is:  12.10155181335347
Stock with Full datasetExperiment  4  for MA : DEMA the MAE is:  10.943210296434415
Stock with Full datasetExperiment  4  for MA : KAMA the MSE  is:  19.64215945229788
Stock with Full datasetExperiment  4  for MA : KAMA the RMSE is:  4.4319475913302355
Stock with Full datasetExperiment  4  for MA : KAMA the MAE is:  3.5686191181651687
Stock with Full datasetExperiment  4  for MA : MIDPOINT the MSE  is:  19.83404242536117
Stock with Full datasetExperiment  4  for MA : MIDPOINT the RMSE is:  4.453542682557468
Stock with Full datasetExperiment  4  for MA : MIDPOINT the MAE is:  3.5743844299716057
Stock with Full datasetExperiment  4  for MA : T3 the MSE  is:  70.66866288490243
Stock with Full datasetExperiment  4  for MA : T3 the RMSE is:  8.406465540576637
Stock with Full datasetExperiment  4  for MA : T3 the MAE is:  6.802843731006552
Stock with Full datasetExperiment  4  for MA : TEMA the MSE  is:  14.860699364166678
Stock with Full datasetExperiment  4  for MA : TEMA the RMSE is:  3.8549577642519868
Stock with Full datasetExperiment  4  for MA : TEMA the MAE is:  3.1502795604602833
Stock with Full datasetExperiment  5  for MA : SMA the MSE  is:  34.39169744803393
Stock with Full datasetExperiment  5  for MA : SMA the RMSE is:  5.864443490053761
Stock with Full datasetExperiment  5  for MA : SMA the MAE is:  4.893666026892695
Stock with Full datasetExperiment  5  for MA : EMA the MSE  is:  73.04930062485933
Stock with Full datasetExperiment  5  for MA : EMA the RMSE is:  8.546888359213506
Stock with Full datasetExperiment  5  for MA : EMA the MAE is:  6.613879572809731
Stock with Full datasetExperiment  5  for MA : WMA the MSE  is:  70.35376938042184
Stock with Full datasetExperiment  5  for MA : WMA the RMSE is:  8.387715385039114
Stock with Full datasetExperiment  5  for MA : WMA the MAE is:  6.8547592718484545
Stock with Full datasetExperiment  5  for MA : DEMA the MSE  is:  70.24761196199488
Stock with Full datasetExperiment  5  for MA : DEMA the RMSE is:  8.381384847505505
Stock with Full datasetExperiment  5  for MA : DEMA the MAE is:  6.862692730259403
Stock with Full datasetExperiment  5  for MA : KAMA the MSE  is:  27.01407660930758
Stock with Full datasetExperiment  5  for MA : KAMA the RMSE is:  5.1975067685677505
Stock with Full datasetExperiment  5  for MA : KAMA the MAE is:  4.263533603346384
Stock with Full datasetExperiment  5  for MA : MIDPOINT the MSE  is:  37.16076795716489
Stock with Full datasetExperiment  5  for MA : MIDPOINT the RMSE is:  6.095963250969029
Stock with Full datasetExperiment  5  for MA : MIDPOINT the MAE is:  5.0853544537748006
Stock with Full datasetExperiment  5  for MA : T3 the MSE  is:  104.49209322707955
Stock with Full datasetExperiment  5  for MA : T3 the RMSE is:  10.222137409909903
Stock with Full datasetExperiment  5  for MA : T3 the MAE is:  7.958642954509092
Stock with Full datasetExperiment  5  for MA : TEMA the MSE  is:  72.80283545670305
Stock with Full datasetExperiment  5  for MA : TEMA the RMSE is:  8.532457761788397
Stock with Full datasetExperiment  5  for MA : TEMA the MAE is:  7.653550657820228
Stock with Full datasetExperiment  6  for MA : SMA the MSE  is:  75.03401716737034
Stock with Full datasetExperiment  6  for MA : SMA the RMSE is:  8.662217797271685
Stock with Full datasetExperiment  6  for MA : SMA the MAE is:  7.077228582293258
Stock with Full datasetExperiment  6  for MA : EMA the MSE  is:  70.28436187942754
Stock with Full datasetExperiment  6  for MA : EMA the RMSE is:  8.383576914386099
Stock with Full datasetExperiment  6  for MA : EMA the MAE is:  6.876111393338704
Stock with Full datasetExperiment  6  for MA : WMA the MSE  is:  70.57086226636761
Stock with Full datasetExperiment  6  for MA : WMA the RMSE is:  8.400646538592587
Stock with Full datasetExperiment  6  for MA : WMA the MAE is:  6.6664001460728475
Stock with Full datasetExperiment  6  for MA : DEMA the MSE  is:  329.6035699397079
Stock with Full datasetExperiment  6  for MA : DEMA the RMSE is:  18.15498746735199
Stock with Full datasetExperiment  6  for MA : DEMA the MAE is:  16.799244301034683
Stock with Full datasetExperiment  6  for MA : KAMA the MSE  is:  103.27437965196852
Stock with Full datasetExperiment  6  for MA : KAMA the RMSE is:  10.162400289890599
Stock with Full datasetExperiment  6  for MA : KAMA the MAE is:  8.510636158449836
Stock with Full datasetExperiment  6  for MA : MIDPOINT the MSE  is:  97.31838139819504
Stock with Full datasetExperiment  6  for MA : MIDPOINT the RMSE is:  9.86500792692003
Stock with Full datasetExperiment  6  for MA : MIDPOINT the MAE is:  8.251875922025462
Stock with Full datasetExperiment  6  for MA : T3 the MSE  is:  154.17386959926716
Stock with Full datasetExperiment  6  for MA : T3 the RMSE is:  12.41667707558134
Stock with Full datasetExperiment  6  for MA : T3 the MAE is:  10.12780101556255
Stock with Full datasetExperiment  6  for MA : TEMA the MSE  is:  87.38626729945001
Stock with Full datasetExperiment  6  for MA : TEMA the RMSE is:  9.348062221629144
Stock with Full datasetExperiment  6  for MA : TEMA the MAE is:  8.358174186376312
Stock with Full datasetExperiment  7  for MA : SMA the MSE  is:  44.65212926265077
Stock with Full datasetExperiment  7  for MA : SMA the RMSE is:  6.682224873696692
Stock with Full datasetExperiment  7  for MA : SMA the MAE is:  5.204686480071648
Stock with Full datasetExperiment  7  for MA : EMA the MSE  is:  45.539825469272486
Stock with Full datasetExperiment  7  for MA : EMA the RMSE is:  6.748320196113436
Stock with Full datasetExperiment  7  for MA : EMA the MAE is:  5.43245952292463
Stock with Full datasetExperiment  7  for MA : WMA the MSE  is:  42.30488040231578
Stock with Full datasetExperiment  7  for MA : WMA the RMSE is:  6.504220199402522
Stock with Full datasetExperiment  7  for MA : WMA the MAE is:  5.010195929360332
Stock with Full datasetExperiment  7  for MA : DEMA the MSE  is:  23.305922116020078
Stock with Full datasetExperiment  7  for MA : DEMA the RMSE is:  4.827620751055335
Stock with Full datasetExperiment  7  for MA : DEMA the MAE is:  3.7452201197397774
Stock with Full datasetExperiment  7  for MA : KAMA the MSE  is:  18.082341646298453
Stock with Full datasetExperiment  7  for MA : KAMA the RMSE is:  4.252333670621163
Stock with Full datasetExperiment  7  for MA : KAMA the MAE is:  3.4333194517527637
Stock with Full datasetExperiment  7  for MA : MIDPOINT the MSE  is:  91.59813707600279
Stock with Full datasetExperiment  7  for MA : MIDPOINT the RMSE is:  9.57069156727991
Stock with Full datasetExperiment  7  for MA : MIDPOINT the MAE is:  7.718313236319782
Stock with Full datasetExperiment  7  for MA : T3 the MSE  is:  145.19295971499469
Stock with Full datasetExperiment  7  for MA : T3 the RMSE is:  12.049604131049065
Stock with Full datasetExperiment  7  for MA : T3 the MAE is:  9.875885491811884
Stock with Full datasetExperiment  7  for MA : TEMA the MSE  is:  41.1158513706741
Stock with Full datasetExperiment  7  for MA : TEMA the RMSE is:  6.412164328109044
Stock with Full datasetExperiment  7  for MA : TEMA the MAE is:  5.720374187090847
Stock with Full datasetExperiment  8  for MA : SMA the MSE  is:  30.79397335917816
Stock with Full datasetExperiment  8  for MA : SMA the RMSE is:  5.549231780992587
Stock with Full datasetExperiment  8  for MA : SMA the MAE is:  4.345848876898189
Stock with Full datasetExperiment  8  for MA : EMA the MSE  is:  32.37277762407691
Stock with Full datasetExperiment  8  for MA : EMA the RMSE is:  5.689708043834667
Stock with Full datasetExperiment  8  for MA : EMA the MAE is:  4.4297291061987245
Stock with Full datasetExperiment  8  for MA : WMA the MSE  is:  58.067607397948805
Stock with Full datasetExperiment  8  for MA : WMA the RMSE is:  7.620210456276704
Stock with Full datasetExperiment  8  for MA : WMA the MAE is:  6.244282675104111
Stock with Full datasetExperiment  8  for MA : DEMA the MSE  is:  166.4719121939062
Stock with Full datasetExperiment  8  for MA : DEMA the RMSE is:  12.902399474280209
Stock with Full datasetExperiment  8  for MA : DEMA the MAE is:  11.649540302125361
Stock with Full datasetExperiment  8  for MA : KAMA the MSE  is:  17.81489427298047
Stock with Full datasetExperiment  8  for MA : KAMA the RMSE is:  4.220769393485087
Stock with Full datasetExperiment  8  for MA : KAMA the MAE is:  3.4008273908825086
Stock with Full datasetExperiment  8  for MA : MIDPOINT the MSE  is:  18.523766068694844
Stock with Full datasetExperiment  8  for MA : MIDPOINT the RMSE is:  4.303924496165662
Stock with Full datasetExperiment  8  for MA : MIDPOINT the MAE is:  3.4879205441290337
Stock with Full datasetExperiment  8  for MA : T3 the MSE  is:  51.75272426881254
Stock with Full datasetExperiment  8  for MA : T3 the RMSE is:  7.193936632248893
Stock with Full datasetExperiment  8  for MA : T3 the MAE is:  5.759673530885367
Stock with Full datasetExperiment  8  for MA : TEMA the MSE  is:  28.424875173467463
Stock with Full datasetExperiment  8  for MA : TEMA the RMSE is:  5.331498398524327
Stock with Full datasetExperiment  8  for MA : TEMA the MAE is:  4.66633698560039
In [141]:
text = 'Stock with Full dataset '
simulations = [simulation1,simulation2,simulation3,simulation4,simulation5,simulation6,simulation7,simulation8]
for i,simulation in enumerate(simulations):
  for ma in simulation.keys():
    # print(text+'Experiment ',i+1,' for MA :',ma,'the MSE  is: ',simulation[ma]['final']['mse'])
    print(text+'Experiment ',i+1,' for MA :',ma,'the RMSE is: ',simulation[ma]['final']['rmse'])
    # print(text+'Experiment ',i+1,' for MA :',ma,'the MAE is: ',simulation[ma]['final']['mae'])
  for ma in simulation.keys():
    print(text+'Experiment ',i+1,' for MA :',ma,'the MSE  is: ',simulation[ma]['final']['mse'])
    # print(text+'Experiment ',i+1,' for MA :',ma,'the RMSE is: ',simulation[ma]['final']['rmse'])
    # print(text+'Experiment ',i+1,' for MA :',ma,'the MAE is: ',simulation[ma]['final']['mae'])
  for ma in simulation.keys():
    # print(text+'Experiment ',i+1,' for MA :',ma,'the MSE  is: ',simulation[ma]['final']['mse'])
    # print(text+'Experiment ',i+1,' for MA :',ma,'the RMSE is: ',simulation[ma]['final']['rmse'])
    print(text+'Experiment ',i+1,' for MA :',ma,'the MAE is: ',simulation[ma]['final']['mae'])
Stock with Full dataset Experiment  1  for MA : SMA the RMSE is:  5.432255854260365
Stock with Full dataset Experiment  1  for MA : EMA the RMSE is:  5.348202760406646
Stock with Full dataset Experiment  1  for MA : WMA the RMSE is:  8.9847639644493
Stock with Full dataset Experiment  1  for MA : DEMA the RMSE is:  8.654168316183721
Stock with Full dataset Experiment  1  for MA : KAMA the RMSE is:  4.87546037487117
Stock with Full dataset Experiment  1  for MA : MIDPOINT the RMSE is:  7.318272153983071
Stock with Full dataset Experiment  1  for MA : T3 the RMSE is:  6.640488797834165
Stock with Full dataset Experiment  1  for MA : TEMA the RMSE is:  3.0926372716336124
Stock with Full dataset Experiment  1  for MA : SMA the MSE  is:  29.509403666146007
Stock with Full dataset Experiment  1  for MA : EMA the MSE  is:  28.603272766421263
Stock with Full dataset Experiment  1  for MA : WMA the MSE  is:  80.72598349686672
Stock with Full dataset Experiment  1  for MA : DEMA the MSE  is:  74.8946292448382
Stock with Full dataset Experiment  1  for MA : KAMA the MSE  is:  23.77011386693893
Stock with Full dataset Experiment  1  for MA : MIDPOINT the MSE  is:  53.557107319764015
Stock with Full dataset Experiment  1  for MA : T3 the MSE  is:  44.09609147416104
Stock with Full dataset Experiment  1  for MA : TEMA the MSE  is:  9.564405293897392
Stock with Full dataset Experiment  1  for MA : SMA the MAE is:  4.5288133477558885
Stock with Full dataset Experiment  1  for MA : EMA the MAE is:  4.3952252144553965
Stock with Full dataset Experiment  1  for MA : WMA the MAE is:  7.266216353433966
Stock with Full dataset Experiment  1  for MA : DEMA the MAE is:  7.175854729849037
Stock with Full dataset Experiment  1  for MA : KAMA the MAE is:  3.900500517739451
Stock with Full dataset Experiment  1  for MA : MIDPOINT the MAE is:  6.3365268769325365
Stock with Full dataset Experiment  1  for MA : T3 the MAE is:  5.406095596816415
Stock with Full dataset Experiment  1  for MA : TEMA the MAE is:  2.44888799215368
Stock with Full dataset Experiment  2  for MA : SMA the RMSE is:  8.67185704341257
Stock with Full dataset Experiment  2  for MA : EMA the RMSE is:  7.862814198638052
Stock with Full dataset Experiment  2  for MA : WMA the RMSE is:  8.835353415190172
Stock with Full dataset Experiment  2  for MA : DEMA the RMSE is:  12.39330504554288
Stock with Full dataset Experiment  2  for MA : KAMA the RMSE is:  11.013147388994629
Stock with Full dataset Experiment  2  for MA : MIDPOINT the RMSE is:  10.492574800314088
Stock with Full dataset Experiment  2  for MA : T3 the RMSE is:  15.024615520410403
Stock with Full dataset Experiment  2  for MA : TEMA the RMSE is:  12.531951213144364
Stock with Full dataset Experiment  2  for MA : SMA the MSE  is:  75.20110458138421
Stock with Full dataset Experiment  2  for MA : EMA the MSE  is:  61.82384712230415
Stock with Full dataset Experiment  2  for MA : WMA the MSE  is:  78.06346997131263
Stock with Full dataset Experiment  2  for MA : DEMA the MSE  is:  153.59400995187858
Stock with Full dataset Experiment  2  for MA : KAMA the MSE  is:  121.28941541171922
Stock with Full dataset Experiment  2  for MA : MIDPOINT the MSE  is:  110.09412594018622
Stock with Full dataset Experiment  2  for MA : T3 the MSE  is:  225.73907153615718
Stock with Full dataset Experiment  2  for MA : TEMA the MSE  is:  157.04980120863047
Stock with Full dataset Experiment  2  for MA : SMA the MAE is:  7.0799160587584336
Stock with Full dataset Experiment  2  for MA : EMA the MAE is:  6.504666247736678
Stock with Full dataset Experiment  2  for MA : WMA the MAE is:  6.948265794170055
Stock with Full dataset Experiment  2  for MA : DEMA the MAE is:  11.203775482220726
Stock with Full dataset Experiment  2  for MA : KAMA the MAE is:  9.175643045864026
Stock with Full dataset Experiment  2  for MA : MIDPOINT the MAE is:  8.796456301428389
Stock with Full dataset Experiment  2  for MA : T3 the MAE is:  12.611725131734374
Stock with Full dataset Experiment  2  for MA : TEMA the MAE is:  11.294114614846999
Stock with Full dataset Experiment  3  for MA : SMA the RMSE is:  5.7206439706125165
Stock with Full dataset Experiment  3  for MA : EMA the RMSE is:  11.998010631691534
Stock with Full dataset Experiment  3  for MA : WMA the RMSE is:  4.958449798877449
Stock with Full dataset Experiment  3  for MA : DEMA the RMSE is:  14.3963453762824
Stock with Full dataset Experiment  3  for MA : KAMA the RMSE is:  4.872756371610364
Stock with Full dataset Experiment  3  for MA : MIDPOINT the RMSE is:  5.968619684258559
Stock with Full dataset Experiment  3  for MA : T3 the RMSE is:  10.185055542763655
Stock with Full dataset Experiment  3  for MA : TEMA the RMSE is:  6.316228397668807
Stock with Full dataset Experiment  3  for MA : SMA the MSE  is:  32.725767438505336
Stock with Full dataset Experiment  3  for MA : EMA the MSE  is:  143.9522591181831
Stock with Full dataset Experiment  3  for MA : WMA the MSE  is:  24.586224407987817
Stock with Full dataset Experiment  3  for MA : DEMA the MSE  is:  207.2547601932076
Stock with Full dataset Experiment  3  for MA : KAMA the MSE  is:  23.743754657069395
Stock with Full dataset Experiment  3  for MA : MIDPOINT the MSE  is:  35.62442093531873
Stock with Full dataset Experiment  3  for MA : T3 the MSE  is:  103.73535640918065
Stock with Full dataset Experiment  3  for MA : TEMA the MSE  is:  39.894741171517865
Stock with Full dataset Experiment  3  for MA : SMA the MAE is:  4.798603095387009
Stock with Full dataset Experiment  3  for MA : EMA the MAE is:  10.07848404711658
Stock with Full dataset Experiment  3  for MA : WMA the MAE is:  3.970226889097132
Stock with Full dataset Experiment  3  for MA : DEMA the MAE is:  12.894635987621164
Stock with Full dataset Experiment  3  for MA : KAMA the MAE is:  3.7850733762502107
Stock with Full dataset Experiment  3  for MA : MIDPOINT the MAE is:  5.0490603478808165
Stock with Full dataset Experiment  3  for MA : T3 the MAE is:  8.016244139827235
Stock with Full dataset Experiment  3  for MA : TEMA the MAE is:  5.481705479796751
Stock with Full dataset Experiment  4  for MA : SMA the RMSE is:  4.447102943159856
Stock with Full dataset Experiment  4  for MA : EMA the RMSE is:  5.623322106759368
Stock with Full dataset Experiment  4  for MA : WMA the RMSE is:  7.2439857551294375
Stock with Full dataset Experiment  4  for MA : DEMA the RMSE is:  12.10155181335347
Stock with Full dataset Experiment  4  for MA : KAMA the RMSE is:  4.4319475913302355
Stock with Full dataset Experiment  4  for MA : MIDPOINT the RMSE is:  4.453542682557468
Stock with Full dataset Experiment  4  for MA : T3 the RMSE is:  8.406465540576637
Stock with Full dataset Experiment  4  for MA : TEMA the RMSE is:  3.8549577642519868
Stock with Full dataset Experiment  4  for MA : SMA the MSE  is:  19.776724587061057
Stock with Full dataset Experiment  4  for MA : EMA the MSE  is:  31.621751516368622
Stock with Full dataset Experiment  4  for MA : WMA the MSE  is:  52.4753296205182
Stock with Full dataset Experiment  4  for MA : DEMA the MSE  is:  146.44755629127866
Stock with Full dataset Experiment  4  for MA : KAMA the MSE  is:  19.64215945229788
Stock with Full dataset Experiment  4  for MA : MIDPOINT the MSE  is:  19.83404242536117
Stock with Full dataset Experiment  4  for MA : T3 the MSE  is:  70.66866288490243
Stock with Full dataset Experiment  4  for MA : TEMA the MSE  is:  14.860699364166678
Stock with Full dataset Experiment  4  for MA : SMA the MAE is:  3.587879520041786
Stock with Full dataset Experiment  4  for MA : EMA the MAE is:  4.355106062590965
Stock with Full dataset Experiment  4  for MA : WMA the MAE is:  5.852253139584933
Stock with Full dataset Experiment  4  for MA : DEMA the MAE is:  10.943210296434415
Stock with Full dataset Experiment  4  for MA : KAMA the MAE is:  3.5686191181651687
Stock with Full dataset Experiment  4  for MA : MIDPOINT the MAE is:  3.5743844299716057
Stock with Full dataset Experiment  4  for MA : T3 the MAE is:  6.802843731006552
Stock with Full dataset Experiment  4  for MA : TEMA the MAE is:  3.1502795604602833
Stock with Full dataset Experiment  5  for MA : SMA the RMSE is:  5.864443490053761
Stock with Full dataset Experiment  5  for MA : EMA the RMSE is:  8.546888359213506
Stock with Full dataset Experiment  5  for MA : WMA the RMSE is:  8.387715385039114
Stock with Full dataset Experiment  5  for MA : DEMA the RMSE is:  8.381384847505505
Stock with Full dataset Experiment  5  for MA : KAMA the RMSE is:  5.1975067685677505
Stock with Full dataset Experiment  5  for MA : MIDPOINT the RMSE is:  6.095963250969029
Stock with Full dataset Experiment  5  for MA : T3 the RMSE is:  10.222137409909903
Stock with Full dataset Experiment  5  for MA : TEMA the RMSE is:  8.532457761788397
Stock with Full dataset Experiment  5  for MA : SMA the MSE  is:  34.39169744803393
Stock with Full dataset Experiment  5  for MA : EMA the MSE  is:  73.04930062485933
Stock with Full dataset Experiment  5  for MA : WMA the MSE  is:  70.35376938042184
Stock with Full dataset Experiment  5  for MA : DEMA the MSE  is:  70.24761196199488
Stock with Full dataset Experiment  5  for MA : KAMA the MSE  is:  27.01407660930758
Stock with Full dataset Experiment  5  for MA : MIDPOINT the MSE  is:  37.16076795716489
Stock with Full dataset Experiment  5  for MA : T3 the MSE  is:  104.49209322707955
Stock with Full dataset Experiment  5  for MA : TEMA the MSE  is:  72.80283545670305
Stock with Full dataset Experiment  5  for MA : SMA the MAE is:  4.893666026892695
Stock with Full dataset Experiment  5  for MA : EMA the MAE is:  6.613879572809731
Stock with Full dataset Experiment  5  for MA : WMA the MAE is:  6.8547592718484545
Stock with Full dataset Experiment  5  for MA : DEMA the MAE is:  6.862692730259403
Stock with Full dataset Experiment  5  for MA : KAMA the MAE is:  4.263533603346384
Stock with Full dataset Experiment  5  for MA : MIDPOINT the MAE is:  5.0853544537748006
Stock with Full dataset Experiment  5  for MA : T3 the MAE is:  7.958642954509092
Stock with Full dataset Experiment  5  for MA : TEMA the MAE is:  7.653550657820228
Stock with Full dataset Experiment  6  for MA : SMA the RMSE is:  8.662217797271685
Stock with Full dataset Experiment  6  for MA : EMA the RMSE is:  8.383576914386099
Stock with Full dataset Experiment  6  for MA : WMA the RMSE is:  8.400646538592587
Stock with Full dataset Experiment  6  for MA : DEMA the RMSE is:  18.15498746735199
Stock with Full dataset Experiment  6  for MA : KAMA the RMSE is:  10.162400289890599
Stock with Full dataset Experiment  6  for MA : MIDPOINT the RMSE is:  9.86500792692003
Stock with Full dataset Experiment  6  for MA : T3 the RMSE is:  12.41667707558134
Stock with Full dataset Experiment  6  for MA : TEMA the RMSE is:  9.348062221629144
Stock with Full dataset Experiment  6  for MA : SMA the MSE  is:  75.03401716737034
Stock with Full dataset Experiment  6  for MA : EMA the MSE  is:  70.28436187942754
Stock with Full dataset Experiment  6  for MA : WMA the MSE  is:  70.57086226636761
Stock with Full dataset Experiment  6  for MA : DEMA the MSE  is:  329.6035699397079
Stock with Full dataset Experiment  6  for MA : KAMA the MSE  is:  103.27437965196852
Stock with Full dataset Experiment  6  for MA : MIDPOINT the MSE  is:  97.31838139819504
Stock with Full dataset Experiment  6  for MA : T3 the MSE  is:  154.17386959926716
Stock with Full dataset Experiment  6  for MA : TEMA the MSE  is:  87.38626729945001
Stock with Full dataset Experiment  6  for MA : SMA the MAE is:  7.077228582293258
Stock with Full dataset Experiment  6  for MA : EMA the MAE is:  6.876111393338704
Stock with Full dataset Experiment  6  for MA : WMA the MAE is:  6.6664001460728475
Stock with Full dataset Experiment  6  for MA : DEMA the MAE is:  16.799244301034683
Stock with Full dataset Experiment  6  for MA : KAMA the MAE is:  8.510636158449836
Stock with Full dataset Experiment  6  for MA : MIDPOINT the MAE is:  8.251875922025462
Stock with Full dataset Experiment  6  for MA : T3 the MAE is:  10.12780101556255
Stock with Full dataset Experiment  6  for MA : TEMA the MAE is:  8.358174186376312
Stock with Full dataset Experiment  7  for MA : SMA the RMSE is:  6.682224873696692
Stock with Full dataset Experiment  7  for MA : EMA the RMSE is:  6.748320196113436
Stock with Full dataset Experiment  7  for MA : WMA the RMSE is:  6.504220199402522
Stock with Full dataset Experiment  7  for MA : DEMA the RMSE is:  4.827620751055335
Stock with Full dataset Experiment  7  for MA : KAMA the RMSE is:  4.252333670621163
Stock with Full dataset Experiment  7  for MA : MIDPOINT the RMSE is:  9.57069156727991
Stock with Full dataset Experiment  7  for MA : T3 the RMSE is:  12.049604131049065
Stock with Full dataset Experiment  7  for MA : TEMA the RMSE is:  6.412164328109044
Stock with Full dataset Experiment  7  for MA : SMA the MSE  is:  44.65212926265077
Stock with Full dataset Experiment  7  for MA : EMA the MSE  is:  45.539825469272486
Stock with Full dataset Experiment  7  for MA : WMA the MSE  is:  42.30488040231578
Stock with Full dataset Experiment  7  for MA : DEMA the MSE  is:  23.305922116020078
Stock with Full dataset Experiment  7  for MA : KAMA the MSE  is:  18.082341646298453
Stock with Full dataset Experiment  7  for MA : MIDPOINT the MSE  is:  91.59813707600279
Stock with Full dataset Experiment  7  for MA : T3 the MSE  is:  145.19295971499469
Stock with Full dataset Experiment  7  for MA : TEMA the MSE  is:  41.1158513706741
Stock with Full dataset Experiment  7  for MA : SMA the MAE is:  5.204686480071648
Stock with Full dataset Experiment  7  for MA : EMA the MAE is:  5.43245952292463
Stock with Full dataset Experiment  7  for MA : WMA the MAE is:  5.010195929360332
Stock with Full dataset Experiment  7  for MA : DEMA the MAE is:  3.7452201197397774
Stock with Full dataset Experiment  7  for MA : KAMA the MAE is:  3.4333194517527637
Stock with Full dataset Experiment  7  for MA : MIDPOINT the MAE is:  7.718313236319782
Stock with Full dataset Experiment  7  for MA : T3 the MAE is:  9.875885491811884
Stock with Full dataset Experiment  7  for MA : TEMA the MAE is:  5.720374187090847
Stock with Full dataset Experiment  8  for MA : SMA the RMSE is:  5.549231780992587
Stock with Full dataset Experiment  8  for MA : EMA the RMSE is:  5.689708043834667
Stock with Full dataset Experiment  8  for MA : WMA the RMSE is:  7.620210456276704
Stock with Full dataset Experiment  8  for MA : DEMA the RMSE is:  12.902399474280209
Stock with Full dataset Experiment  8  for MA : KAMA the RMSE is:  4.220769393485087
Stock with Full dataset Experiment  8  for MA : MIDPOINT the RMSE is:  4.303924496165662
Stock with Full dataset Experiment  8  for MA : T3 the RMSE is:  7.193936632248893
Stock with Full dataset Experiment  8  for MA : TEMA the RMSE is:  5.331498398524327
Stock with Full dataset Experiment  8  for MA : SMA the MSE  is:  30.79397335917816
Stock with Full dataset Experiment  8  for MA : EMA the MSE  is:  32.37277762407691
Stock with Full dataset Experiment  8  for MA : WMA the MSE  is:  58.067607397948805
Stock with Full dataset Experiment  8  for MA : DEMA the MSE  is:  166.4719121939062
Stock with Full dataset Experiment  8  for MA : KAMA the MSE  is:  17.81489427298047
Stock with Full dataset Experiment  8  for MA : MIDPOINT the MSE  is:  18.523766068694844
Stock with Full dataset Experiment  8  for MA : T3 the MSE  is:  51.75272426881254
Stock with Full dataset Experiment  8  for MA : TEMA the MSE  is:  28.424875173467463
Stock with Full dataset Experiment  8  for MA : SMA the MAE is:  4.345848876898189
Stock with Full dataset Experiment  8  for MA : EMA the MAE is:  4.4297291061987245
Stock with Full dataset Experiment  8  for MA : WMA the MAE is:  6.244282675104111
Stock with Full dataset Experiment  8  for MA : DEMA the MAE is:  11.649540302125361
Stock with Full dataset Experiment  8  for MA : KAMA the MAE is:  3.4008273908825086
Stock with Full dataset Experiment  8  for MA : MIDPOINT the MAE is:  3.4879205441290337
Stock with Full dataset Experiment  8  for MA : T3 the MAE is:  5.759673530885367
Stock with Full dataset Experiment  8  for MA : TEMA the MAE is:  4.66633698560039

Export HTML

In [1]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [3]:
cd drive/MyDrive/Stock price prediction/Archana - LSTM Hybrid
/content/drive/.shortcut-targets-by-id/1IaGjVBlTspxI2CHSrxfYnaiYvsaG0pHs/Stock price prediction/Archana - LSTM Hybrid
In [ ]:
%%shell
jupyter nbconvert --to html